Libraries¶
In [1]:
import torch
import torch.nn as nn
from torchvision import models, transforms
from torch.utils.data import DataLoader, Dataset
import torchvision.datasets
from torch.utils.data import random_split
import torch.optim as optim
import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
from skimage.transform import resize
from skimage.io import imread
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import ConfusionMatrixDisplay
import seaborn as sns # Beautify CM
from torchvision.models import ResNet50_Weights, VGG16_Weights, EfficientNet_B0_Weights
Preprocessing¶
In [2]:
# Transformations for the images
transform = transforms.Compose([
transforms.Resize((224, 224)), # Resize to 224x224
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # Normalize using ImageNet stats
])
# Load dataset
dataset_path = "C:/Users/rishi/Desktop/JHU/Critical Infrastructure Protection/Major Project/Data Sets/Sonar/TrainSetMotionBlur"
dataset = torchvision.datasets.ImageFolder(root=dataset_path, transform=transform)
# Train-test split
train_size = int(0.8 * len(dataset))
test_size = len(dataset) - train_size
train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
# DataLoaders
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# Classes
class_names = dataset.classes
num_classes = len(class_names)
In [3]:
print(class_names, num_classes)
['BigAnimals', 'Mines', 'Pipes', 'Rockets', 'Vehicles'] 5
Model Setup¶
In [4]:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Running on: ", device)
Running on: cuda
In [7]:
# Set seeds for reproducibility
torch.manual_seed(42) # For PyTorch
np.random.seed(42) # For NumPy
In [8]:
def setup_model_with_dropout(model_type, num_classes, dropout_rate=0.0):
if model_type == 'resnet':
model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
model.fc = nn.Sequential(
nn.Dropout(dropout_rate), # Add dropout before final FC
nn.Linear(model.fc.in_features, num_classes)
)
elif model_type == 'vgg':
model = models.vgg16(weights=models.VGG16_Weights.DEFAULT)
model.classifier[6] = nn.Sequential(
nn.Dropout(dropout_rate), # Add dropout before final FC
nn.Linear(model.classifier[6].in_features, num_classes)
)
elif model_type == 'efficientnet':
model = models.efficientnet_b0(weights=models.EfficientNet_B0_Weights.DEFAULT)
model.classifier = nn.Sequential(
nn.Dropout(dropout_rate), # Add dropout before final FC
nn.Linear(model.classifier[1].in_features, num_classes)
)
model = model.to(device)
return model
Optimizer Setup¶
In [9]:
def setup_optimizer(model, optimizer_name, learning_rate, weight_decay):
if optimizer_name == 'adam':
return optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
elif optimizer_name == 'sgd':
return optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=weight_decay)
elif optimizer_name == 'rmsprop':
return optim.RMSprop(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
else:
raise ValueError(f"Unsupported optimizer: {optimizer_name}")
Training and Evaluation Function¶
In [10]:
def train_and_evaluate(model, optimizer, criterion, train_loader, test_loader, num_epochs=10):
model.train()
for epoch in range(num_epochs):
running_loss = 0.0
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f"Epoch {epoch+1}/{num_epochs}, Loss: {running_loss / len(train_loader)}")
model.eval()
y_true, y_pred = [], []
with torch.no_grad():
for inputs, labels in test_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
_, predictions = torch.max(outputs, 1)
y_true.extend(labels.cpu().numpy())
y_pred.extend(predictions.cpu().numpy())
accuracy = accuracy_score(y_true, y_pred)
cm = confusion_matrix(y_true, y_pred)
report = classification_report(y_true, y_pred, target_names=class_names, zero_division = 1)
print(f"Accuracy: {accuracy * 100:.2f}%")
print("Classification Report:\n", report)
plt.figure(figsize=(10, 8))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix')
plt.show()
return accuracy, report, cm
Hyperparameter Search¶
In [11]:
def hyperparameter_search(model_types, param_grid, train_dataset, test_dataset, num_classes):
best_model, best_metrics, best_params = None, {'accuracy': 0}, None
for model_type in model_types:
for lr in param_grid['learning_rate']:
for batch_size in param_grid['batch_size']:
for opt in param_grid['optimizer']:
for wd in param_grid['weight_decay']:
for dr in param_grid['dropout_rate']:
print(f"Training {model_type} with lr={lr}, batch_size={batch_size}, "
f"optimizer={opt}, weight_decay={wd}, dropout_rate={dr}")
# Create DataLoaders
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
# Set up the model with the specified dropout rate
model = setup_model_with_dropout(model_type, num_classes, dr)
# Set up the optimizer
optimizer = setup_optimizer(model, opt, lr, wd)
# Define the loss function
criterion = nn.CrossEntropyLoss()
# Train and evaluate
accuracy, _, _ = train_and_evaluate(
model, optimizer, criterion, train_loader, test_loader
)
# Track the best model
if accuracy > best_metrics['accuracy']:
best_model = model
best_metrics['accuracy'] = accuracy
best_params = {
'model': model_type,
'learning_rate': lr,
'batch_size': batch_size,
'optimizer': opt,
'weight_decay': wd,
'dropout_rate': dr
}
print(f"Best Model: {best_params['model']} | Accuracy: {best_metrics['accuracy']:.2f}% | Params: {best_params}")
return best_model, best_metrics, best_params
Defining Hyperparameter Grid and Running the Models¶
In [12]:
param_grid = {
'learning_rate': [0.001, 0.0005, 0.0001],
'batch_size': [16, 32, 64],
'optimizer': ['adam', 'sgd', 'rmsprop'],
'weight_decay': [0.0, 1e-4, 1e-3],
'dropout_rate': [0.0, 0.3, 0.5]
}
model_types = ['resnet', 'vgg', 'efficientnet']
best_model, best_metrics, best_params = hyperparameter_search(
model_types, param_grid, train_dataset, test_dataset, num_classes
)
# Save the best model
torch.save(best_model.state_dict(), f"best_model_{best_params['model']}.pth")
print("Best model saved successfully!")
Training resnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.4226167268223233
Epoch 2/10, Loss: 1.064807312356101
Epoch 3/10, Loss: 0.8907065325313144
Epoch 4/10, Loss: 0.8274083733558655
Epoch 5/10, Loss: 0.6042677437265714
Epoch 6/10, Loss: 0.6789631719390551
Epoch 7/10, Loss: 0.5500290770497587
Epoch 8/10, Loss: 0.4349922442601787
Epoch 9/10, Loss: 0.4354906040761206
Epoch 10/10, Loss: 0.32993256486952305
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.55 0.63 11
Mines 0.75 0.63 0.69 19
Pipes 0.88 0.82 0.85 17
Rockets 0.45 0.91 0.61 11
Vehicles 0.88 0.58 0.70 12
accuracy 0.70 70
macro avg 0.74 0.70 0.69 70
weighted avg 0.76 0.70 0.71 70
Training resnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.162516188290384
Epoch 2/10, Loss: 0.9404450787438287
Epoch 3/10, Loss: 0.5829256524642309
Epoch 4/10, Loss: 0.738079573545191
Epoch 5/10, Loss: 0.4737313182817565
Epoch 6/10, Loss: 0.2142927392075459
Epoch 7/10, Loss: 0.15698119439184666
Epoch 8/10, Loss: 0.30264253324518603
Epoch 9/10, Loss: 0.3030023028453191
Epoch 10/10, Loss: 0.276163334854775
Accuracy: 74.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 0.82 0.75 11
Mines 1.00 0.42 0.59 19
Pipes 0.61 1.00 0.76 17
Rockets 0.80 0.73 0.76 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.74 70
macro avg 0.80 0.76 0.75 70
weighted avg 0.81 0.74 0.73 70
Training resnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.3983533051278856
Epoch 2/10, Loss: 0.7916058633062575
Epoch 3/10, Loss: 0.8933926026026408
Epoch 4/10, Loss: 0.6838194727897644
Epoch 5/10, Loss: 0.6425449649492899
Epoch 6/10, Loss: 0.5057451609108183
Epoch 7/10, Loss: 0.48947153488794964
Epoch 8/10, Loss: 0.2554271484000815
Epoch 9/10, Loss: 0.2746824228929149
Epoch 10/10, Loss: 0.31304313087215024
Accuracy: 78.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.91 0.69 11
Mines 0.88 0.79 0.83 19
Pipes 0.94 0.88 0.91 17
Rockets 0.64 0.64 0.64 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.79 70
macro avg 0.80 0.78 0.77 70
weighted avg 0.83 0.79 0.79 70
Training resnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.193955808877945
Epoch 2/10, Loss: 0.8686565442217721
Epoch 3/10, Loss: 0.7154402765962813
Epoch 4/10, Loss: 0.5626552287075255
Epoch 5/10, Loss: 0.4301767258180512
Epoch 6/10, Loss: 0.341320570351349
Epoch 7/10, Loss: 0.3723193659550614
Epoch 8/10, Loss: 0.2415904640737507
Epoch 9/10, Loss: 0.13053373568173912
Epoch 10/10, Loss: 0.2285459830891341
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.88 0.64 0.74 11
Mines 0.76 0.84 0.80 19
Pipes 1.00 0.94 0.97 17
Rockets 0.75 0.82 0.78 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.83 70
macro avg 0.83 0.81 0.82 70
weighted avg 0.84 0.83 0.83 70
Training resnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3468499349223242
Epoch 2/10, Loss: 0.9414312210347917
Epoch 3/10, Loss: 0.7709920803705851
Epoch 4/10, Loss: 0.5886638263861338
Epoch 5/10, Loss: 0.3070489809744888
Epoch 6/10, Loss: 0.4753173639376958
Epoch 7/10, Loss: 0.3401246445460452
Epoch 8/10, Loss: 0.18201635653773943
Epoch 9/10, Loss: 0.19121312893306217
Epoch 10/10, Loss: 0.2542307372722361
Accuracy: 65.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.18 0.31 11
Mines 0.50 0.89 0.64 19
Pipes 1.00 0.82 0.90 17
Rockets 0.62 0.45 0.53 11
Vehicles 0.67 0.67 0.67 12
accuracy 0.66 70
macro avg 0.76 0.60 0.61 70
weighted avg 0.75 0.66 0.64 70
Training resnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3025224374400244
Epoch 2/10, Loss: 0.8990547673569785
Epoch 3/10, Loss: 0.7085604270299276
Epoch 4/10, Loss: 0.5290212598111894
Epoch 5/10, Loss: 0.4095746684405539
Epoch 6/10, Loss: 0.5472410544753075
Epoch 7/10, Loss: 0.2965755129439963
Epoch 8/10, Loss: 0.29717597189462847
Epoch 9/10, Loss: 0.2295161440140671
Epoch 10/10, Loss: 0.212235393623511
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.89 0.73 0.80 11
Mines 0.63 0.89 0.74 19
Pipes 1.00 0.82 0.90 17
Rockets 0.67 0.55 0.60 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.77 70
macro avg 0.80 0.75 0.76 70
weighted avg 0.80 0.77 0.77 70
Training resnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.2990308105945587
Epoch 2/10, Loss: 0.9530814819865756
Epoch 3/10, Loss: 0.5953774419095781
Epoch 4/10, Loss: 0.48136866754955715
Epoch 5/10, Loss: 0.4698384954697556
Epoch 6/10, Loss: 0.41774962345759076
Epoch 7/10, Loss: 0.43096932851605946
Epoch 8/10, Loss: 0.34274400853448445
Epoch 9/10, Loss: 0.3461839560833242
Epoch 10/10, Loss: 0.2830766240755717
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.45 0.91 0.61 11
Mines 0.83 0.53 0.65 19
Pipes 1.00 0.76 0.87 17
Rockets 0.80 0.73 0.76 11
Vehicles 0.69 0.75 0.72 12
accuracy 0.71 70
macro avg 0.76 0.74 0.72 70
weighted avg 0.78 0.71 0.72 70
Training resnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3330308828088973
Epoch 2/10, Loss: 1.0557886792553797
Epoch 3/10, Loss: 0.9798804554674361
Epoch 4/10, Loss: 0.6880867994493909
Epoch 5/10, Loss: 0.5080874214569727
Epoch 6/10, Loss: 0.5979864440030522
Epoch 7/10, Loss: 0.34178847074508667
Epoch 8/10, Loss: 0.31436599377128815
Epoch 9/10, Loss: 0.5198746778898768
Epoch 10/10, Loss: 0.393432407743401
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.45 0.59 11
Mines 0.65 0.89 0.76 19
Pipes 0.74 0.82 0.78 17
Rockets 0.83 0.45 0.59 11
Vehicles 0.69 0.75 0.72 12
accuracy 0.71 70
macro avg 0.75 0.68 0.69 70
weighted avg 0.74 0.71 0.70 70
Training resnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3946256306436327
Epoch 2/10, Loss: 0.9117740227116479
Epoch 3/10, Loss: 0.9644500994020038
Epoch 4/10, Loss: 0.6329642915063434
Epoch 5/10, Loss: 0.5945399800936381
Epoch 6/10, Loss: 0.5637176624602742
Epoch 7/10, Loss: 0.48064622200197643
Epoch 8/10, Loss: 0.5740980307261149
Epoch 9/10, Loss: 0.5354663423366017
Epoch 10/10, Loss: 0.39513255490197075
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.55 0.60 11
Mines 0.59 1.00 0.75 19
Pipes 0.93 0.82 0.88 17
Rockets 0.67 0.73 0.70 11
Vehicles 1.00 0.17 0.29 12
accuracy 0.70 70
macro avg 0.77 0.65 0.64 70
weighted avg 0.77 0.70 0.67 70
Training resnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6025307443406847
Epoch 2/10, Loss: 1.5405692325698004
Epoch 3/10, Loss: 1.4435244997342427
Epoch 4/10, Loss: 1.3230764932102628
Epoch 5/10, Loss: 1.1616113119655185
Epoch 6/10, Loss: 0.9617549545235105
Epoch 7/10, Loss: 0.7858256134721968
Epoch 8/10, Loss: 0.6490684035751555
Epoch 9/10, Loss: 0.48043371323082185
Epoch 10/10, Loss: 0.3300580067767037
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.85 0.89 0.87 19
Pipes 1.00 0.76 0.87 17
Rockets 0.56 0.82 0.67 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.81 70
macro avg 0.83 0.81 0.81 70
weighted avg 0.85 0.81 0.82 70
Training resnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.5914756854375203
Epoch 2/10, Loss: 1.5065450337198045
Epoch 3/10, Loss: 1.4121188984976873
Epoch 4/10, Loss: 1.3099510272343953
Epoch 5/10, Loss: 1.145267817709181
Epoch 6/10, Loss: 1.0113245877954695
Epoch 7/10, Loss: 0.8574566410647498
Epoch 8/10, Loss: 0.6890765329202017
Epoch 9/10, Loss: 0.5953934474123849
Epoch 10/10, Loss: 0.42346658143732285
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.93 0.74 0.82 19
Pipes 0.94 0.88 0.91 17
Rockets 0.64 0.64 0.64 11
Vehicles 0.62 0.67 0.64 12
accuracy 0.77 70
macro avg 0.76 0.77 0.76 70
weighted avg 0.79 0.77 0.77 70
Training resnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6090938250223796
Epoch 2/10, Loss: 1.5209574169582791
Epoch 3/10, Loss: 1.4090070459577773
Epoch 4/10, Loss: 1.2842968040042453
Epoch 5/10, Loss: 1.1461044450600941
Epoch 6/10, Loss: 1.0084151526292164
Epoch 7/10, Loss: 0.8042803605397543
Epoch 8/10, Loss: 0.6644364694754282
Epoch 9/10, Loss: 0.5289792981412675
Epoch 10/10, Loss: 0.3747177827689383
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.62 0.91 0.74 11
Mines 0.92 0.63 0.75 19
Pipes 0.93 0.76 0.84 17
Rockets 0.37 0.64 0.47 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.71 70
macro avg 0.77 0.72 0.72 70
weighted avg 0.80 0.71 0.73 70
Training resnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6048787699805365
Epoch 2/10, Loss: 1.518365138106876
Epoch 3/10, Loss: 1.4223630759451125
Epoch 4/10, Loss: 1.3102718856599596
Epoch 5/10, Loss: 1.124075127972497
Epoch 6/10, Loss: 0.9711402323510911
Epoch 7/10, Loss: 0.7369612753391266
Epoch 8/10, Loss: 0.5739545126756033
Epoch 9/10, Loss: 0.4391527507040236
Epoch 10/10, Loss: 0.301627023352517
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 1.00 0.68 0.81 19
Pipes 1.00 0.88 0.94 17
Rockets 0.59 0.91 0.71 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.83 70
macro avg 0.84 0.84 0.83 70
weighted avg 0.87 0.83 0.83 70
Training resnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6056244174639385
Epoch 2/10, Loss: 1.5209917955928378
Epoch 3/10, Loss: 1.4197064439455669
Epoch 4/10, Loss: 1.3131271733178034
Epoch 5/10, Loss: 1.1430712673399184
Epoch 6/10, Loss: 0.9676694174607595
Epoch 7/10, Loss: 0.8248189687728882
Epoch 8/10, Loss: 0.6425560944610171
Epoch 9/10, Loss: 0.508629611796803
Epoch 10/10, Loss: 0.4010121259424422
Accuracy: 74.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.58 1.00 0.73 11
Mines 0.81 0.68 0.74 19
Pipes 0.87 0.76 0.81 17
Rockets 0.64 0.64 0.64 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.74 70
macro avg 0.76 0.75 0.74 70
weighted avg 0.77 0.74 0.74 70
Training resnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.599039249949985
Epoch 2/10, Loss: 1.5465318494372897
Epoch 3/10, Loss: 1.4834294782744513
Epoch 4/10, Loss: 1.3679198688930936
Epoch 5/10, Loss: 1.2596847547425165
Epoch 6/10, Loss: 1.0577468342251248
Epoch 7/10, Loss: 0.9246773223082224
Epoch 8/10, Loss: 0.7261453800731235
Epoch 9/10, Loss: 0.5998371375931634
Epoch 10/10, Loss: 0.47361570596694946
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.91 0.69 11
Mines 0.67 0.84 0.74 19
Pipes 0.91 0.59 0.71 17
Rockets 0.67 0.55 0.60 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.71 70
macro avg 0.76 0.71 0.71 70
weighted avg 0.77 0.71 0.72 70
Training resnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5932846069335938
Epoch 2/10, Loss: 1.5125007629394531
Epoch 3/10, Loss: 1.4343340926700168
Epoch 4/10, Loss: 1.3152380519443088
Epoch 5/10, Loss: 1.1779274344444275
Epoch 6/10, Loss: 1.0102976693047419
Epoch 7/10, Loss: 0.8080636064211527
Epoch 8/10, Loss: 0.6247726844416724
Epoch 9/10, Loss: 0.4865005314350128
Epoch 10/10, Loss: 0.37168965571456486
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.59 0.91 0.71 11
Mines 0.78 0.74 0.76 19
Pipes 0.93 0.82 0.88 17
Rockets 0.64 0.64 0.64 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.77 70
macro avg 0.79 0.77 0.77 70
weighted avg 0.80 0.77 0.78 70
Training resnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5942732956674364
Epoch 2/10, Loss: 1.520610021220313
Epoch 3/10, Loss: 1.4188056389490764
Epoch 4/10, Loss: 1.3110480970806546
Epoch 5/10, Loss: 1.1567334168487124
Epoch 6/10, Loss: 0.9366027745935652
Epoch 7/10, Loss: 0.7455500596099429
Epoch 8/10, Loss: 0.5629456556505628
Epoch 9/10, Loss: 0.4098606093062295
Epoch 10/10, Loss: 0.3741333683331807
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.89 0.84 0.86 19
Pipes 0.88 0.82 0.85 17
Rockets 0.69 1.00 0.81 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.86 70
macro avg 0.87 0.86 0.86 70
weighted avg 0.88 0.86 0.86 70
Training resnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6141408019595676
Epoch 2/10, Loss: 1.5412682361072965
Epoch 3/10, Loss: 1.4311500655280218
Epoch 4/10, Loss: 1.339218384689755
Epoch 5/10, Loss: 1.2026889721552532
Epoch 6/10, Loss: 0.9942514532142215
Epoch 7/10, Loss: 0.7807694441742368
Epoch 8/10, Loss: 0.5717482484049268
Epoch 9/10, Loss: 0.43164928754170734
Epoch 10/10, Loss: 0.34235500130388474
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.83 1.00 0.90 19
Pipes 1.00 0.71 0.83 17
Rockets 0.67 0.73 0.70 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.83 70
macro avg 0.83 0.82 0.82 70
weighted avg 0.85 0.83 0.83 70
Training resnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.734106421470642
Epoch 2/10, Loss: 1.632602592309316
Epoch 3/10, Loss: 1.5056263870663114
Epoch 4/10, Loss: 1.3478395806418524
Epoch 5/10, Loss: 1.307608723640442
Epoch 6/10, Loss: 1.277146617571513
Epoch 7/10, Loss: 1.1896857718626659
Epoch 8/10, Loss: 1.1302101049158308
Epoch 9/10, Loss: 1.0378981663121118
Epoch 10/10, Loss: 1.0534772442446814
Accuracy: 47.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.18 0.31 11
Mines 0.29 0.21 0.24 19
Pipes 0.65 0.65 0.65 17
Rockets 0.47 0.82 0.60 11
Vehicles 0.39 0.58 0.47 12
accuracy 0.47 70
macro avg 0.56 0.49 0.45 70
weighted avg 0.53 0.47 0.45 70
Training resnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.8758017553223505
Epoch 2/10, Loss: 1.5887501902050443
Epoch 3/10, Loss: 1.6351321273379855
Epoch 4/10, Loss: 1.5267871816953023
Epoch 5/10, Loss: 1.5086637139320374
Epoch 6/10, Loss: 1.4019915858904521
Epoch 7/10, Loss: 1.3977366818322077
Epoch 8/10, Loss: 1.3991872403356764
Epoch 9/10, Loss: 1.3068819840749104
Epoch 10/10, Loss: 1.2084604832861159
Accuracy: 31.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.40 0.18 0.25 11
Mines 1.00 0.00 0.00 19
Pipes 0.50 0.76 0.60 17
Rockets 0.17 0.55 0.26 11
Vehicles 0.25 0.08 0.12 12
accuracy 0.31 70
macro avg 0.46 0.32 0.25 70
weighted avg 0.53 0.31 0.25 70
Training resnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.8445361521508958
Epoch 2/10, Loss: 1.5993913743231032
Epoch 3/10, Loss: 1.4822213053703308
Epoch 4/10, Loss: 1.447070598602295
Epoch 5/10, Loss: 1.3964167833328247
Epoch 6/10, Loss: 1.3226625422636669
Epoch 7/10, Loss: 1.280195050769382
Epoch 8/10, Loss: 1.246349897649553
Epoch 9/10, Loss: 1.3959194355540805
Epoch 10/10, Loss: 1.2048158513175116
Accuracy: 38.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.29 0.91 0.44 11
Mines 1.00 0.05 0.10 19
Pipes 0.38 0.65 0.48 17
Rockets 0.50 0.09 0.15 11
Vehicles 1.00 0.33 0.50 12
accuracy 0.39 70
macro avg 0.63 0.41 0.34 70
weighted avg 0.66 0.39 0.32 70
Training resnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6907083127233717
Epoch 2/10, Loss: 1.5496970746252272
Epoch 3/10, Loss: 1.5319243139690824
Epoch 4/10, Loss: 1.2478582395447626
Epoch 5/10, Loss: 1.1820433272255793
Epoch 6/10, Loss: 1.0938162008921306
Epoch 7/10, Loss: 1.078911476665073
Epoch 8/10, Loss: 1.025193105141322
Epoch 9/10, Loss: 0.922495370109876
Epoch 10/10, Loss: 0.8603832225004832
Accuracy: 34.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.64 0.78 11
Mines 1.00 0.00 0.00 19
Pipes 0.44 0.24 0.31 17
Rockets 0.20 0.91 0.32 11
Vehicles 1.00 0.25 0.40 12
accuracy 0.34 70
macro avg 0.73 0.41 0.36 70
weighted avg 0.74 0.34 0.32 70
Training resnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.8155369559923809
Epoch 2/10, Loss: 1.698067671722836
Epoch 3/10, Loss: 1.613488992055257
Epoch 4/10, Loss: 1.5560898648367987
Epoch 5/10, Loss: 1.525249759356181
Epoch 6/10, Loss: 1.4714729719691806
Epoch 7/10, Loss: 1.4264371063974168
Epoch 8/10, Loss: 1.4917979372872248
Epoch 9/10, Loss: 1.4182882110277812
Epoch 10/10, Loss: 1.3364136550161574
Accuracy: 31.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.19 0.27 0.22 11
Mines 0.34 0.58 0.43 19
Pipes 1.00 0.00 0.00 17
Rockets 0.30 0.27 0.29 11
Vehicles 0.42 0.42 0.42 12
accuracy 0.31 70
macro avg 0.45 0.31 0.27 70
weighted avg 0.48 0.31 0.27 70
Training resnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.7503236598438687
Epoch 2/10, Loss: 1.525853004720476
Epoch 3/10, Loss: 1.6457813713285658
Epoch 4/10, Loss: 1.581303444173601
Epoch 5/10, Loss: 1.5090147654215496
Epoch 6/10, Loss: 1.533726069662306
Epoch 7/10, Loss: 1.5263812210824754
Epoch 8/10, Loss: 1.564587328169081
Epoch 9/10, Loss: 1.462156315644582
Epoch 10/10, Loss: 1.4250423577096727
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.09 0.17 11
Mines 0.25 0.26 0.26 19
Pipes 1.00 0.00 0.00 17
Rockets 0.14 0.55 0.23 11
Vehicles 0.57 0.33 0.42 12
accuracy 0.23 70
macro avg 0.59 0.25 0.21 70
weighted avg 0.59 0.23 0.20 70
Training resnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.725001871585846
Epoch 2/10, Loss: 1.660434239440494
Epoch 3/10, Loss: 1.639801561832428
Epoch 4/10, Loss: 1.4418346484502156
Epoch 5/10, Loss: 1.3926312923431396
Epoch 6/10, Loss: 1.4204682641559176
Epoch 7/10, Loss: 1.4347761736975775
Epoch 8/10, Loss: 1.40579840209749
Epoch 9/10, Loss: 1.4227964348263211
Epoch 10/10, Loss: 1.2864632076687283
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.16 0.92 0.27 12
accuracy 0.16 70
macro avg 0.63 0.18 0.05 70
weighted avg 0.70 0.16 0.05 70
Training resnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7739845249387953
Epoch 2/10, Loss: 1.4908165269427829
Epoch 3/10, Loss: 1.3431430459022522
Epoch 4/10, Loss: 1.6922807561026678
Epoch 5/10, Loss: 1.7025073170661926
Epoch 6/10, Loss: 1.4602694776323106
Epoch 7/10, Loss: 1.4071754680739508
Epoch 8/10, Loss: 1.5877947211265564
Epoch 9/10, Loss: 1.6899099482430353
Epoch 10/10, Loss: 1.617710338698493
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 0.67 0.12 0.20 17
Rockets 0.23 0.64 0.33 11
Vehicles 0.22 0.67 0.33 12
accuracy 0.24 70
macro avg 0.62 0.28 0.17 70
weighted avg 0.66 0.24 0.16 70
Training resnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.8560697833697002
Epoch 2/10, Loss: 1.72652753856447
Epoch 3/10, Loss: 1.519673744837443
Epoch 4/10, Loss: 1.6964555713865492
Epoch 5/10, Loss: 1.6123443444569905
Epoch 6/10, Loss: 1.6021656460232205
Epoch 7/10, Loss: 1.4689137869411044
Epoch 8/10, Loss: 1.723332855436537
Epoch 9/10, Loss: 1.4460946321487427
Epoch 10/10, Loss: 1.6339329613579645
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.18 0.27 11
Mines 0.33 0.53 0.41 19
Pipes 1.00 0.00 0.00 17
Rockets 0.21 0.55 0.31 11
Vehicles 0.12 0.08 0.10 12
accuracy 0.27 70
macro avg 0.43 0.27 0.22 70
weighted avg 0.47 0.27 0.22 70
Training resnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.318136625819736
Epoch 2/10, Loss: 0.6271341277493371
Epoch 3/10, Loss: 0.5273193187183804
Epoch 4/10, Loss: 0.29870442549387616
Epoch 5/10, Loss: 0.2154626233710183
Epoch 6/10, Loss: 0.3095477885670132
Epoch 7/10, Loss: 0.23052839934825897
Epoch 8/10, Loss: 0.14967088504797882
Epoch 9/10, Loss: 0.1994265429675579
Epoch 10/10, Loss: 0.13322077650162908
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.82 0.78 11
Mines 0.81 0.68 0.74 19
Pipes 0.70 0.94 0.80 17
Rockets 0.70 0.64 0.67 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.77 70
macro avg 0.79 0.77 0.77 70
weighted avg 0.79 0.77 0.77 70
Training resnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.2787310878435771
Epoch 2/10, Loss: 0.6070606145593855
Epoch 3/10, Loss: 0.4307821426126692
Epoch 4/10, Loss: 0.4720744540294011
Epoch 5/10, Loss: 0.2747436331378089
Epoch 6/10, Loss: 0.21444267075922754
Epoch 7/10, Loss: 0.0918874785097109
Epoch 8/10, Loss: 0.08035216170052688
Epoch 9/10, Loss: 0.14382295207016998
Epoch 10/10, Loss: 0.1957029580242104
Accuracy: 55.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.28 0.64 0.39 11
Mines 1.00 0.53 0.69 19
Pipes 0.80 0.47 0.59 17
Rockets 0.50 0.64 0.56 11
Vehicles 0.64 0.58 0.61 12
accuracy 0.56 70
macro avg 0.64 0.57 0.57 70
weighted avg 0.70 0.56 0.58 70
Training resnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.4254873726103041
Epoch 2/10, Loss: 0.7442313598261939
Epoch 3/10, Loss: 0.439843797021442
Epoch 4/10, Loss: 0.3478623496161567
Epoch 5/10, Loss: 0.27363450328509015
Epoch 6/10, Loss: 0.21000471504198182
Epoch 7/10, Loss: 0.26192094799545074
Epoch 8/10, Loss: 0.12505430272883838
Epoch 9/10, Loss: 0.18707665180166563
Epoch 10/10, Loss: 0.17002554858724275
Accuracy: 74.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.47 0.82 0.60 11
Mines 0.93 0.68 0.79 19
Pipes 1.00 0.71 0.83 17
Rockets 0.60 0.82 0.69 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.74 70
macro avg 0.78 0.76 0.75 70
weighted avg 0.82 0.74 0.76 70
Training resnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.2266347010930378
Epoch 2/10, Loss: 0.5174893405702379
Epoch 3/10, Loss: 0.420554604795244
Epoch 4/10, Loss: 0.24855494168069628
Epoch 5/10, Loss: 0.17683126073744562
Epoch 6/10, Loss: 0.1832921008268992
Epoch 7/10, Loss: 0.2156809867463178
Epoch 8/10, Loss: 0.3905339472823673
Epoch 9/10, Loss: 0.26458174569739235
Epoch 10/10, Loss: 0.16510788102944693
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.89 0.84 0.86 19
Pipes 0.88 0.82 0.85 17
Rockets 0.80 0.73 0.76 11
Vehicles 0.71 0.83 0.77 12
accuracy 0.83 70
macro avg 0.82 0.83 0.82 70
weighted avg 0.83 0.83 0.83 70
Training resnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3095429407225714
Epoch 2/10, Loss: 0.7222234209378561
Epoch 3/10, Loss: 0.5748671823077731
Epoch 4/10, Loss: 0.4789412534899182
Epoch 5/10, Loss: 0.379461783501837
Epoch 6/10, Loss: 0.15521379394663704
Epoch 7/10, Loss: 0.13818020621935526
Epoch 8/10, Loss: 0.07150079806645711
Epoch 9/10, Loss: 0.1192985512316227
Epoch 10/10, Loss: 0.12117369550590713
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.88 0.74 0.80 19
Pipes 0.91 0.59 0.71 17
Rockets 0.53 0.82 0.64 11
Vehicles 0.67 0.83 0.74 12
accuracy 0.76 70
macro avg 0.78 0.78 0.76 70
weighted avg 0.80 0.76 0.76 70
Training resnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.358589185608758
Epoch 2/10, Loss: 0.6573142740461562
Epoch 3/10, Loss: 0.357602059841156
Epoch 4/10, Loss: 0.29794391244649887
Epoch 5/10, Loss: 0.2970869408713447
Epoch 6/10, Loss: 0.1741111328204473
Epoch 7/10, Loss: 0.2648878447297547
Epoch 8/10, Loss: 0.3319832815064324
Epoch 9/10, Loss: 0.21796965599060059
Epoch 10/10, Loss: 0.116624323444234
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.82 0.78 11
Mines 0.70 0.84 0.76 19
Pipes 1.00 0.53 0.69 17
Rockets 0.60 0.82 0.69 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.76 70
macro avg 0.79 0.77 0.76 70
weighted avg 0.80 0.76 0.76 70
Training resnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.2935337490505643
Epoch 2/10, Loss: 0.9011543790499369
Epoch 3/10, Loss: 0.42074071367581684
Epoch 4/10, Loss: 0.29023962799045777
Epoch 5/10, Loss: 0.3926093810134464
Epoch 6/10, Loss: 0.3479352427853478
Epoch 7/10, Loss: 0.12580815371539858
Epoch 8/10, Loss: 0.19967379627956283
Epoch 9/10, Loss: 0.29156384699874455
Epoch 10/10, Loss: 0.4296848459376229
Accuracy: 67.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.36 0.53 11
Mines 0.80 0.42 0.55 19
Pipes 0.65 0.88 0.75 17
Rockets 0.53 0.91 0.67 11
Vehicles 0.71 0.83 0.77 12
accuracy 0.67 70
macro avg 0.74 0.68 0.65 70
weighted avg 0.74 0.67 0.65 70
Training resnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.2713403304417927
Epoch 2/10, Loss: 0.6217876341607835
Epoch 3/10, Loss: 0.5034871465630002
Epoch 4/10, Loss: 0.3906996597846349
Epoch 5/10, Loss: 0.3597744305928548
Epoch 6/10, Loss: 0.32037991947597927
Epoch 7/10, Loss: 0.2765474087662167
Epoch 8/10, Loss: 0.2581599574122164
Epoch 9/10, Loss: 0.14151777947942415
Epoch 10/10, Loss: 0.17788495837400356
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.80 0.73 0.76 11
Mines 0.68 0.89 0.77 19
Pipes 0.86 0.71 0.77 17
Rockets 0.67 0.73 0.70 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.77 70
macro avg 0.80 0.76 0.77 70
weighted avg 0.79 0.77 0.77 70
Training resnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.2658422589302063
Epoch 2/10, Loss: 0.581815328862932
Epoch 3/10, Loss: 0.5531101756625705
Epoch 4/10, Loss: 0.5398479600747427
Epoch 5/10, Loss: 0.2914842532740699
Epoch 6/10, Loss: 0.15915695081154504
Epoch 7/10, Loss: 0.21822172610296142
Epoch 8/10, Loss: 0.22310814054475891
Epoch 9/10, Loss: 0.3029838990834024
Epoch 10/10, Loss: 0.2842514328658581
Accuracy: 65.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.57 0.73 0.64 11
Mines 0.53 0.89 0.67 19
Pipes 1.00 0.59 0.74 17
Rockets 0.57 0.36 0.44 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.66 70
macro avg 0.73 0.63 0.65 70
weighted avg 0.74 0.66 0.66 70
Training resnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6112354728910658
Epoch 2/10, Loss: 1.5575438208050199
Epoch 3/10, Loss: 1.5046111742655437
Epoch 4/10, Loss: 1.4430170059204102
Epoch 5/10, Loss: 1.372162593735589
Epoch 6/10, Loss: 1.282157791985406
Epoch 7/10, Loss: 1.1752833392884996
Epoch 8/10, Loss: 1.0693223476409912
Epoch 9/10, Loss: 0.9543312788009644
Epoch 10/10, Loss: 0.8025830056932237
Accuracy: 55.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.64 0.64 11
Mines 0.77 0.53 0.62 19
Pipes 1.00 0.35 0.52 17
Rockets 0.44 0.64 0.52 11
Vehicles 0.38 0.75 0.50 12
accuracy 0.56 70
macro avg 0.64 0.58 0.56 70
weighted avg 0.68 0.56 0.56 70
Training resnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6181188159518771
Epoch 2/10, Loss: 1.567995203865899
Epoch 3/10, Loss: 1.513802170753479
Epoch 4/10, Loss: 1.4620558420817058
Epoch 5/10, Loss: 1.4085653225580852
Epoch 6/10, Loss: 1.314110689693027
Epoch 7/10, Loss: 1.248077392578125
Epoch 8/10, Loss: 1.15117433336046
Epoch 9/10, Loss: 1.0440964963701036
Epoch 10/10, Loss: 0.9352103471755981
Accuracy: 55.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.36 0.91 0.51 11
Mines 0.82 0.47 0.60 19
Pipes 1.00 0.35 0.52 17
Rockets 0.45 0.45 0.45 11
Vehicles 0.64 0.75 0.69 12
accuracy 0.56 70
macro avg 0.65 0.59 0.56 70
weighted avg 0.70 0.56 0.56 70
Training resnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.620799011654324
Epoch 2/10, Loss: 1.5536230537626479
Epoch 3/10, Loss: 1.5231299930148654
Epoch 4/10, Loss: 1.4501039054658678
Epoch 5/10, Loss: 1.3856360250049167
Epoch 6/10, Loss: 1.30223720603519
Epoch 7/10, Loss: 1.2199738952848647
Epoch 8/10, Loss: 1.1449093023935955
Epoch 9/10, Loss: 1.0142190721299913
Epoch 10/10, Loss: 0.892806715435452
Accuracy: 61.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.53 0.82 0.64 11
Mines 0.64 0.74 0.68 19
Pipes 1.00 0.29 0.45 17
Rockets 0.58 0.64 0.61 11
Vehicles 0.57 0.67 0.62 12
accuracy 0.61 70
macro avg 0.66 0.63 0.60 70
weighted avg 0.69 0.61 0.60 70
Training resnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6084594859017267
Epoch 2/10, Loss: 1.5695985290739272
Epoch 3/10, Loss: 1.5098208056555853
Epoch 4/10, Loss: 1.469975060886807
Epoch 5/10, Loss: 1.400799724790785
Epoch 6/10, Loss: 1.3044244315889146
Epoch 7/10, Loss: 1.1859809822506375
Epoch 8/10, Loss: 1.0789310998386807
Epoch 9/10, Loss: 0.9672550625271268
Epoch 10/10, Loss: 0.830731537606981
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.82 0.67 11
Mines 0.74 0.74 0.74 19
Pipes 1.00 0.53 0.69 17
Rockets 0.57 0.73 0.64 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.70 70
macro avg 0.72 0.71 0.70 70
weighted avg 0.75 0.70 0.70 70
Training resnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6208589606814914
Epoch 2/10, Loss: 1.5816940863927205
Epoch 3/10, Loss: 1.5297937658098009
Epoch 4/10, Loss: 1.470865249633789
Epoch 5/10, Loss: 1.407602760526869
Epoch 6/10, Loss: 1.3478700055016413
Epoch 7/10, Loss: 1.2482484843995836
Epoch 8/10, Loss: 1.1556239525477092
Epoch 9/10, Loss: 1.0179137256410387
Epoch 10/10, Loss: 0.8963104354010688
Accuracy: 65.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 1.00 0.47 0.64 19
Pipes 1.00 0.65 0.79 17
Rockets 0.40 0.73 0.52 11
Vehicles 0.53 0.67 0.59 12
accuracy 0.66 70
macro avg 0.72 0.68 0.66 70
weighted avg 0.77 0.66 0.67 70
Training resnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.604387256834242
Epoch 2/10, Loss: 1.556163165304396
Epoch 3/10, Loss: 1.5178654458787706
Epoch 4/10, Loss: 1.4500008821487427
Epoch 5/10, Loss: 1.3905557658937242
Epoch 6/10, Loss: 1.3411810663011339
Epoch 7/10, Loss: 1.2533339262008667
Epoch 8/10, Loss: 1.1894085539711847
Epoch 9/10, Loss: 1.0992150439156427
Epoch 10/10, Loss: 0.9883739815817939
Accuracy: 55.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.73 0.59 11
Mines 0.78 0.37 0.50 19
Pipes 1.00 0.53 0.69 17
Rockets 0.40 0.55 0.46 11
Vehicles 0.43 0.75 0.55 12
accuracy 0.56 70
macro avg 0.62 0.58 0.56 70
weighted avg 0.67 0.56 0.56 70
Training resnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.605827530225118
Epoch 2/10, Loss: 1.568408105108473
Epoch 3/10, Loss: 1.521109978357951
Epoch 4/10, Loss: 1.4500228696399264
Epoch 5/10, Loss: 1.3921703365114
Epoch 6/10, Loss: 1.3119384050369263
Epoch 7/10, Loss: 1.2057998577753704
Epoch 8/10, Loss: 1.0823393265406291
Epoch 9/10, Loss: 0.9847709139188131
Epoch 10/10, Loss: 0.8599712120162116
Accuracy: 68.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.48 0.91 0.62 11
Mines 1.00 0.68 0.81 19
Pipes 1.00 0.53 0.69 17
Rockets 0.47 0.73 0.57 11
Vehicles 0.80 0.67 0.73 12
accuracy 0.69 70
macro avg 0.75 0.70 0.69 70
weighted avg 0.80 0.69 0.70 70
Training resnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.631524231698778
Epoch 2/10, Loss: 1.58484861585829
Epoch 3/10, Loss: 1.5015964375601873
Epoch 4/10, Loss: 1.4515250391430325
Epoch 5/10, Loss: 1.3596396181318495
Epoch 6/10, Loss: 1.261790050400628
Epoch 7/10, Loss: 1.1973362631267972
Epoch 8/10, Loss: 1.0717651976479425
Epoch 9/10, Loss: 0.9780026541815864
Epoch 10/10, Loss: 0.8657756116655138
Accuracy: 62.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.53 0.73 0.62 11
Mines 0.73 0.58 0.65 19
Pipes 0.91 0.59 0.71 17
Rockets 0.60 0.55 0.57 11
Vehicles 0.47 0.75 0.58 12
accuracy 0.63 70
macro avg 0.65 0.64 0.63 70
weighted avg 0.68 0.63 0.64 70
Training resnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6060575644175212
Epoch 2/10, Loss: 1.5709816482332017
Epoch 3/10, Loss: 1.5261682404412165
Epoch 4/10, Loss: 1.4567123651504517
Epoch 5/10, Loss: 1.3882799413469102
Epoch 6/10, Loss: 1.3191834158367581
Epoch 7/10, Loss: 1.274588942527771
Epoch 8/10, Loss: 1.1653894186019897
Epoch 9/10, Loss: 1.0742917723125882
Epoch 10/10, Loss: 0.9302143255869547
Accuracy: 57.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.47 0.82 0.60 11
Mines 1.00 0.42 0.59 19
Pipes 1.00 0.47 0.64 17
Rockets 0.45 0.45 0.45 11
Vehicles 0.42 0.83 0.56 12
accuracy 0.57 70
macro avg 0.67 0.60 0.57 70
weighted avg 0.73 0.57 0.58 70
Training resnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 2.2364389896392822
Epoch 2/10, Loss: 1.5726801024542914
Epoch 3/10, Loss: 1.3985052108764648
Epoch 4/10, Loss: 1.343801909022861
Epoch 5/10, Loss: 1.293658971786499
Epoch 6/10, Loss: 1.2267332474390666
Epoch 7/10, Loss: 1.237774862183465
Epoch 8/10, Loss: 1.173316048251258
Epoch 9/10, Loss: 1.1640650629997253
Epoch 10/10, Loss: 1.086770600742764
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.00 0.00 0.00 11
Mines 0.31 0.58 0.41 19
Pipes 1.00 0.12 0.21 17
Rockets 0.00 0.00 0.00 11
Vehicles 0.21 0.50 0.30 12
accuracy 0.27 70
macro avg 0.31 0.24 0.18 70
weighted avg 0.36 0.27 0.21 70
Training resnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.808372179667155
Epoch 2/10, Loss: 1.6230721871058147
Epoch 3/10, Loss: 1.565709286265903
Epoch 4/10, Loss: 1.4929688506656222
Epoch 5/10, Loss: 1.3427786032358806
Epoch 6/10, Loss: 1.3040700223710802
Epoch 7/10, Loss: 1.2454583909776475
Epoch 8/10, Loss: 1.198132660653856
Epoch 9/10, Loss: 1.143845213784112
Epoch 10/10, Loss: 1.076713052060869
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.18 0.31 11
Mines 0.37 0.53 0.43 19
Pipes 0.00 0.00 0.00 17
Rockets 0.29 0.55 0.38 11
Vehicles 0.42 0.67 0.52 12
accuracy 0.37 70
macro avg 0.42 0.38 0.33 70
weighted avg 0.37 0.37 0.31 70
Training resnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.717261552810669
Epoch 2/10, Loss: 1.7136245833502874
Epoch 3/10, Loss: 1.6535462273491754
Epoch 4/10, Loss: 1.5904622077941895
Epoch 5/10, Loss: 1.5012637774149578
Epoch 6/10, Loss: 1.4336439768473308
Epoch 7/10, Loss: 1.4684309826956854
Epoch 8/10, Loss: 1.4174211422602336
Epoch 9/10, Loss: 1.3798016839557223
Epoch 10/10, Loss: 1.4175612264209323
Accuracy: 18.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.18 0.29 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.17 1.00 0.29 11
Vehicles 0.00 0.00 0.00 12
accuracy 0.19 70
macro avg 0.57 0.24 0.12 70
weighted avg 0.65 0.19 0.09 70
Training resnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.7774546808666654
Epoch 2/10, Loss: 1.5155029296875
Epoch 3/10, Loss: 1.4477375083499484
Epoch 4/10, Loss: 1.4054959879981146
Epoch 5/10, Loss: 1.3783313168419733
Epoch 6/10, Loss: 1.6138630443149142
Epoch 7/10, Loss: 1.5246189037958782
Epoch 8/10, Loss: 1.582943081855774
Epoch 9/10, Loss: 1.422645092010498
Epoch 10/10, Loss: 1.4441760381062825
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.20 0.91 0.33 11
Vehicles 0.35 0.58 0.44 12
accuracy 0.24 70
macro avg 0.71 0.30 0.15 70
weighted avg 0.76 0.24 0.13 70
Training resnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7961189084582858
Epoch 2/10, Loss: 1.6105302042431302
Epoch 3/10, Loss: 1.5761767095989652
Epoch 4/10, Loss: 1.4050338533189561
Epoch 5/10, Loss: 1.3972088098526
Epoch 6/10, Loss: 1.2688374651802912
Epoch 7/10, Loss: 1.2477614217334323
Epoch 8/10, Loss: 1.2539477745691936
Epoch 9/10, Loss: 1.184810823864407
Epoch 10/10, Loss: 1.2480953137079875
Accuracy: 42.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.24 0.45 0.31 11
Mines 0.62 0.42 0.50 19
Pipes 0.56 0.59 0.57 17
Rockets 0.00 0.00 0.00 11
Vehicles 0.41 0.58 0.48 12
accuracy 0.43 70
macro avg 0.36 0.41 0.37 70
weighted avg 0.41 0.43 0.41 70
Training resnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.8132809003194172
Epoch 2/10, Loss: 1.6969143946965535
Epoch 3/10, Loss: 1.4890205065409343
Epoch 4/10, Loss: 1.5385922061072455
Epoch 5/10, Loss: 1.406441781255934
Epoch 6/10, Loss: 1.4351407554414537
Epoch 7/10, Loss: 1.387596673435635
Epoch 8/10, Loss: 1.3961236874262493
Epoch 9/10, Loss: 1.4216380649142795
Epoch 10/10, Loss: 1.2876145707236395
Accuracy: 30.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 0.57 0.24 0.33 17
Rockets 0.40 0.55 0.46 11
Vehicles 0.23 0.92 0.37 12
accuracy 0.30 70
macro avg 0.64 0.34 0.23 70
weighted avg 0.67 0.30 0.22 70
Training resnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 2.1542109648386636
Epoch 2/10, Loss: 1.6565896537568834
Epoch 3/10, Loss: 1.4980413251452975
Epoch 4/10, Loss: 1.4883364174101088
Epoch 5/10, Loss: 1.5369706021414862
Epoch 6/10, Loss: 1.4179984993404813
Epoch 7/10, Loss: 1.4809291627671983
Epoch 8/10, Loss: 1.408834973971049
Epoch 9/10, Loss: 1.2741835514704387
Epoch 10/10, Loss: 1.208282682630751
Accuracy: 28.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.00 0.00 0.00 11
Mines 0.30 0.89 0.45 19
Pipes 1.00 0.06 0.11 17
Rockets 0.50 0.18 0.27 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.29 70
macro avg 0.56 0.23 0.17 70
weighted avg 0.57 0.29 0.19 70
Training resnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.837659133805169
Epoch 2/10, Loss: 1.7246218125025432
Epoch 3/10, Loss: 1.7088880009121366
Epoch 4/10, Loss: 1.6751013331943088
Epoch 5/10, Loss: 1.6165935860739813
Epoch 6/10, Loss: 1.7176596456103854
Epoch 7/10, Loss: 1.6028056939442952
Epoch 8/10, Loss: 1.4993082814746432
Epoch 9/10, Loss: 1.483764370282491
Epoch 10/10, Loss: 1.3745291762881808
Accuracy: 20.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.22 0.64 0.33 11
Vehicles 0.18 0.58 0.28 12
accuracy 0.20 70
macro avg 0.68 0.24 0.12 70
weighted avg 0.74 0.20 0.10 70
Training resnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.8044664329952664
Epoch 2/10, Loss: 1.8941419257058039
Epoch 3/10, Loss: 1.8292007711198595
Epoch 4/10, Loss: 1.7594659063551161
Epoch 5/10, Loss: 1.73043778207567
Epoch 6/10, Loss: 1.648239466879103
Epoch 7/10, Loss: 1.8565068509843614
Epoch 8/10, Loss: 1.6748281849755182
Epoch 9/10, Loss: 1.8722615904278226
Epoch 10/10, Loss: 1.797177129321628
Accuracy: 18.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.19 0.45 0.27 11
Mines 0.40 0.11 0.17 19
Pipes 1.00 0.00 0.00 17
Rockets 0.00 0.00 0.00 11
Vehicles 0.16 0.50 0.24 12
accuracy 0.19 70
macro avg 0.35 0.21 0.14 70
weighted avg 0.41 0.19 0.13 70
Training resnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.4037375688552856
Epoch 2/10, Loss: 0.5238921582698822
Epoch 3/10, Loss: 0.2782835364341736
Epoch 4/10, Loss: 0.384238538146019
Epoch 5/10, Loss: 0.25264913141727446
Epoch 6/10, Loss: 0.11714325696229935
Epoch 7/10, Loss: 0.09257960952818393
Epoch 8/10, Loss: 0.1696735978126526
Epoch 9/10, Loss: 0.22484682127833366
Epoch 10/10, Loss: 0.22467032372951506
Accuracy: 41.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.23 0.91 0.36 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.71 0.83 17
Rockets 0.50 0.55 0.52 11
Vehicles 0.50 0.08 0.14 12
accuracy 0.41 70
macro avg 0.65 0.45 0.37 70
weighted avg 0.71 0.41 0.36 70
Training resnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.367081618309021
Epoch 2/10, Loss: 0.6363498389720916
Epoch 3/10, Loss: 0.26164103448390963
Epoch 4/10, Loss: 0.10002669841051101
Epoch 5/10, Loss: 0.19851963743567466
Epoch 6/10, Loss: 0.48175930678844453
Epoch 7/10, Loss: 0.29471192359924314
Epoch 8/10, Loss: 0.2386631965637207
Epoch 9/10, Loss: 0.34482542872428895
Epoch 10/10, Loss: 0.16516066193580628
Accuracy: 65.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.36 0.47 11
Mines 0.65 0.68 0.67 19
Pipes 1.00 0.76 0.87 17
Rockets 0.45 0.82 0.58 11
Vehicles 0.64 0.58 0.61 12
accuracy 0.66 70
macro avg 0.68 0.64 0.64 70
weighted avg 0.70 0.66 0.66 70
Training resnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.404780960083008
Epoch 2/10, Loss: 0.7457653999328613
Epoch 3/10, Loss: 0.573503029346466
Epoch 4/10, Loss: 0.37469319701194764
Epoch 5/10, Loss: 0.1510464608669281
Epoch 6/10, Loss: 0.08069640249013901
Epoch 7/10, Loss: 0.11081529036164284
Epoch 8/10, Loss: 0.09876922406256199
Epoch 9/10, Loss: 0.12294882833957672
Epoch 10/10, Loss: 0.2601924419403076
Accuracy: 58.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.31 0.91 0.47 11
Mines 0.86 0.32 0.46 19
Pipes 1.00 0.71 0.83 17
Rockets 0.69 0.82 0.75 11
Vehicles 0.67 0.33 0.44 12
accuracy 0.59 70
macro avg 0.71 0.62 0.59 70
weighted avg 0.75 0.59 0.59 70
Training resnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.3923875093460083
Epoch 2/10, Loss: 0.7448350071907044
Epoch 3/10, Loss: 0.48575612902641296
Epoch 4/10, Loss: 0.2832310751080513
Epoch 5/10, Loss: 0.21242095977067948
Epoch 6/10, Loss: 0.15307264775037766
Epoch 7/10, Loss: 0.168168106675148
Epoch 8/10, Loss: 0.21982236504554747
Epoch 9/10, Loss: 0.11308542788028716
Epoch 10/10, Loss: 0.09966732263565063
Accuracy: 74.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.86 0.55 0.67 11
Mines 0.61 1.00 0.76 19
Pipes 0.86 0.71 0.77 17
Rockets 0.70 0.64 0.67 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.74 70
macro avg 0.81 0.71 0.73 70
weighted avg 0.79 0.74 0.74 70
Training resnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.464220929145813
Epoch 2/10, Loss: 0.7200254440307617
Epoch 3/10, Loss: 0.3509207397699356
Epoch 4/10, Loss: 0.2490429848432541
Epoch 5/10, Loss: 0.23006482571363449
Epoch 6/10, Loss: 0.16621556729078293
Epoch 7/10, Loss: 0.11722920015454293
Epoch 8/10, Loss: 0.09187766537070274
Epoch 9/10, Loss: 0.16392970606684684
Epoch 10/10, Loss: 0.19464632570743562
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.55 0.71 11
Mines 0.70 0.84 0.76 19
Pipes 1.00 0.53 0.69 17
Rockets 0.53 0.82 0.64 11
Vehicles 0.60 0.75 0.67 12
accuracy 0.70 70
macro avg 0.77 0.70 0.69 70
weighted avg 0.77 0.70 0.70 70
Training resnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.419085144996643
Epoch 2/10, Loss: 0.6637660145759583
Epoch 3/10, Loss: 0.3476736456155777
Epoch 4/10, Loss: 0.3188014060258865
Epoch 5/10, Loss: 0.2342240571975708
Epoch 6/10, Loss: 0.2359367400407791
Epoch 7/10, Loss: 0.20686642676591874
Epoch 8/10, Loss: 0.19795167446136475
Epoch 9/10, Loss: 0.13857455402612687
Epoch 10/10, Loss: 0.15650096014142037
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.53 0.82 0.64 11
Mines 0.73 0.84 0.78 19
Pipes 0.81 0.76 0.79 17
Rockets 0.70 0.64 0.67 11
Vehicles 1.00 0.42 0.59 12
accuracy 0.71 70
macro avg 0.75 0.70 0.69 70
weighted avg 0.76 0.71 0.71 70
Training resnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.376357412338257
Epoch 2/10, Loss: 0.560101842880249
Epoch 3/10, Loss: 0.18354561179876328
Epoch 4/10, Loss: 0.22067804485559464
Epoch 5/10, Loss: 0.1850912258028984
Epoch 6/10, Loss: 0.19419077634811402
Epoch 7/10, Loss: 0.3343463957309723
Epoch 8/10, Loss: 0.18880865424871446
Epoch 9/10, Loss: 0.10863943845033645
Epoch 10/10, Loss: 0.09640386328101158
Accuracy: 65.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.42 0.91 0.57 11
Mines 0.92 0.63 0.75 19
Pipes 1.00 0.53 0.69 17
Rockets 0.78 0.64 0.70 11
Vehicles 0.53 0.67 0.59 12
accuracy 0.66 70
macro avg 0.73 0.67 0.66 70
weighted avg 0.77 0.66 0.67 70
Training resnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.410058283805847
Epoch 2/10, Loss: 0.6304821968078613
Epoch 3/10, Loss: 0.3040381073951721
Epoch 4/10, Loss: 0.21664910465478898
Epoch 5/10, Loss: 0.35163146555423735
Epoch 6/10, Loss: 0.2485546126961708
Epoch 7/10, Loss: 0.26674160957336424
Epoch 8/10, Loss: 0.2366854280233383
Epoch 9/10, Loss: 0.27479947805404664
Epoch 10/10, Loss: 0.13874648362398148
Accuracy: 72.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.82 0.72 11
Mines 1.00 0.63 0.77 19
Pipes 0.79 0.88 0.83 17
Rockets 0.50 0.73 0.59 11
Vehicles 0.78 0.58 0.67 12
accuracy 0.73 70
macro avg 0.74 0.73 0.72 70
weighted avg 0.78 0.73 0.73 70
Training resnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.4393911123275758
Epoch 2/10, Loss: 0.6483575344085694
Epoch 3/10, Loss: 0.25982457399368286
Epoch 4/10, Loss: 0.2447027236223221
Epoch 5/10, Loss: 0.1950637385249138
Epoch 6/10, Loss: 0.14911128282546998
Epoch 7/10, Loss: 0.13636105954647065
Epoch 8/10, Loss: 0.17083117514848709
Epoch 9/10, Loss: 0.27291951030492784
Epoch 10/10, Loss: 0.27879289239645005
Accuracy: 62.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.53 0.82 0.64 11
Mines 0.65 0.58 0.61 19
Pipes 1.00 0.59 0.74 17
Rockets 0.35 0.55 0.43 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.63 70
macro avg 0.68 0.64 0.64 70
weighted avg 0.71 0.63 0.64 70
Training resnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6192580699920653
Epoch 2/10, Loss: 1.6029924869537353
Epoch 3/10, Loss: 1.5624946117401124
Epoch 4/10, Loss: 1.5314868688583374
Epoch 5/10, Loss: 1.498932695388794
Epoch 6/10, Loss: 1.4709229946136475
Epoch 7/10, Loss: 1.4171667575836182
Epoch 8/10, Loss: 1.351966667175293
Epoch 9/10, Loss: 1.3145070791244506
Epoch 10/10, Loss: 1.244429612159729
Accuracy: 51.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.44 1.00 0.61 11
Mines 0.56 0.53 0.54 19
Pipes 1.00 0.35 0.52 17
Rockets 0.50 0.18 0.27 11
Vehicles 0.41 0.58 0.48 12
accuracy 0.51 70
macro avg 0.58 0.53 0.48 70
weighted avg 0.61 0.51 0.49 70
Training resnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6173524141311646
Epoch 2/10, Loss: 1.6066046714782716
Epoch 3/10, Loss: 1.590271782875061
Epoch 4/10, Loss: 1.563883090019226
Epoch 5/10, Loss: 1.5154938220977783
Epoch 6/10, Loss: 1.4903292179107666
Epoch 7/10, Loss: 1.4310560941696167
Epoch 8/10, Loss: 1.4163599967956544
Epoch 9/10, Loss: 1.3376551389694213
Epoch 10/10, Loss: 1.3133774280548096
Accuracy: 28.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.33 0.55 0.41 11
Mines 0.50 0.11 0.17 19
Pipes 1.00 0.06 0.11 17
Rockets 0.25 0.18 0.21 11
Vehicles 0.23 0.75 0.35 12
accuracy 0.29 70
macro avg 0.46 0.33 0.25 70
weighted avg 0.51 0.29 0.23 70
Training resnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.5993676662445069
Epoch 2/10, Loss: 1.6116870403289796
Epoch 3/10, Loss: 1.5962719678878785
Epoch 4/10, Loss: 1.5267730951309204
Epoch 5/10, Loss: 1.5168847799301148
Epoch 6/10, Loss: 1.482055139541626
Epoch 7/10, Loss: 1.4540302753448486
Epoch 8/10, Loss: 1.4160171270370483
Epoch 9/10, Loss: 1.384578824043274
Epoch 10/10, Loss: 1.3216362714767456
Accuracy: 40.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.47 0.64 0.54 11
Mines 0.67 0.42 0.52 19
Pipes 1.00 0.06 0.11 17
Rockets 0.33 0.18 0.24 11
Vehicles 0.28 0.83 0.42 12
accuracy 0.40 70
macro avg 0.55 0.43 0.36 70
weighted avg 0.60 0.40 0.36 70
Training resnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5999752759933472
Epoch 2/10, Loss: 1.5711183309555055
Epoch 3/10, Loss: 1.5503682851791383
Epoch 4/10, Loss: 1.5155330419540405
Epoch 5/10, Loss: 1.484725022315979
Epoch 6/10, Loss: 1.4138214826583861
Epoch 7/10, Loss: 1.3558192014694215
Epoch 8/10, Loss: 1.335634183883667
Epoch 9/10, Loss: 1.2614092111587525
Epoch 10/10, Loss: 1.215025496482849
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.40 0.36 0.38 11
Mines 0.29 0.11 0.15 19
Pipes 1.00 0.06 0.11 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.23 1.00 0.38 12
accuracy 0.27 70
macro avg 0.58 0.31 0.20 70
weighted avg 0.58 0.27 0.19 70
Training resnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6143107652664184
Epoch 2/10, Loss: 1.5951984643936157
Epoch 3/10, Loss: 1.569461989402771
Epoch 4/10, Loss: 1.554451012611389
Epoch 5/10, Loss: 1.5054109573364258
Epoch 6/10, Loss: 1.4638803720474243
Epoch 7/10, Loss: 1.4374160051345826
Epoch 8/10, Loss: 1.3995794534683228
Epoch 9/10, Loss: 1.3159393072128296
Epoch 10/10, Loss: 1.2974696636199952
Accuracy: 32.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.25 0.64 0.36 11
Mines 0.50 0.05 0.10 19
Pipes 0.75 0.18 0.29 17
Rockets 0.50 0.18 0.27 11
Vehicles 0.31 0.83 0.45 12
accuracy 0.33 70
macro avg 0.46 0.38 0.29 70
weighted avg 0.49 0.33 0.27 70
Training resnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.613374900817871
Epoch 2/10, Loss: 1.5868376970291138
Epoch 3/10, Loss: 1.5613632202148438
Epoch 4/10, Loss: 1.5351258277893067
Epoch 5/10, Loss: 1.5152001142501832
Epoch 6/10, Loss: 1.4652099847793578
Epoch 7/10, Loss: 1.4195740222930908
Epoch 8/10, Loss: 1.385606575012207
Epoch 9/10, Loss: 1.3756380796432495
Epoch 10/10, Loss: 1.310046124458313
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.47 0.82 0.60 11
Mines 1.00 0.16 0.27 19
Pipes 1.00 0.12 0.21 17
Rockets 0.33 0.18 0.24 11
Vehicles 0.25 0.83 0.38 12
accuracy 0.37 70
macro avg 0.61 0.42 0.34 70
weighted avg 0.68 0.37 0.32 70
Training resnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6056535482406615
Epoch 2/10, Loss: 1.5893643140792846
Epoch 3/10, Loss: 1.565538477897644
Epoch 4/10, Loss: 1.526033353805542
Epoch 5/10, Loss: 1.4798139333724976
Epoch 6/10, Loss: 1.4307512760162353
Epoch 7/10, Loss: 1.4018553256988526
Epoch 8/10, Loss: 1.3301554679870606
Epoch 9/10, Loss: 1.2833974599838256
Epoch 10/10, Loss: 1.2187736749649047
Accuracy: 48.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.82 0.62 11
Mines 0.62 0.42 0.50 19
Pipes 1.00 0.24 0.38 17
Rockets 0.43 0.27 0.33 11
Vehicles 0.36 0.83 0.50 12
accuracy 0.49 70
macro avg 0.58 0.52 0.47 70
weighted avg 0.62 0.49 0.46 70
Training resnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6164644241333008
Epoch 2/10, Loss: 1.5882964134216309
Epoch 3/10, Loss: 1.5853560447692872
Epoch 4/10, Loss: 1.5381868600845336
Epoch 5/10, Loss: 1.5087300062179565
Epoch 6/10, Loss: 1.4666438341140746
Epoch 7/10, Loss: 1.42217059135437
Epoch 8/10, Loss: 1.3791276454925536
Epoch 9/10, Loss: 1.2992891550064087
Epoch 10/10, Loss: 1.2319360017776488
Accuracy: 47.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.43 0.82 0.56 11
Mines 0.57 0.21 0.31 19
Pipes 1.00 0.29 0.45 17
Rockets 0.45 0.45 0.45 11
Vehicles 0.38 0.83 0.53 12
accuracy 0.47 70
macro avg 0.57 0.52 0.46 70
weighted avg 0.60 0.47 0.44 70
Training resnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6182761907577514
Epoch 2/10, Loss: 1.6154759168624877
Epoch 3/10, Loss: 1.5869584798812866
Epoch 4/10, Loss: 1.5429916381835938
Epoch 5/10, Loss: 1.5213533401489259
Epoch 6/10, Loss: 1.4788875102996826
Epoch 7/10, Loss: 1.4710569381713867
Epoch 8/10, Loss: 1.396748685836792
Epoch 9/10, Loss: 1.3510621547698975
Epoch 10/10, Loss: 1.3111457824707031
Accuracy: 44.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.60 0.55 0.57 11
Mines 0.46 0.32 0.38 19
Pipes 1.00 0.24 0.38 17
Rockets 0.38 0.55 0.44 11
Vehicles 0.33 0.75 0.46 12
accuracy 0.44 70
macro avg 0.55 0.48 0.45 70
weighted avg 0.58 0.44 0.43 70
Training resnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.8339087247848511
Epoch 2/10, Loss: 1.792039942741394
Epoch 3/10, Loss: 1.6390167951583863
Epoch 4/10, Loss: 1.539816951751709
Epoch 5/10, Loss: 1.4677112817764282
Epoch 6/10, Loss: 1.5050673246383668
Epoch 7/10, Loss: 1.5249444484710692
Epoch 8/10, Loss: 1.4648377418518066
Epoch 9/10, Loss: 1.4491739749908448
Epoch 10/10, Loss: 1.3893022775650024
Accuracy: 21.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.22 0.55 0.32 11
Vehicles 0.21 0.75 0.33 12
accuracy 0.21 70
macro avg 0.69 0.26 0.13 70
weighted avg 0.74 0.21 0.11 70
Training resnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.8116579055786133
Epoch 2/10, Loss: 1.7387785196304322
Epoch 3/10, Loss: 1.4832184553146361
Epoch 4/10, Loss: 1.593901801109314
Epoch 5/10, Loss: 1.4272387266159057
Epoch 6/10, Loss: 1.3356574535369874
Epoch 7/10, Loss: 1.2463066101074218
Epoch 8/10, Loss: 1.2186163187026977
Epoch 9/10, Loss: 1.1554844379425049
Epoch 10/10, Loss: 1.1494877338409424
Accuracy: 31.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.18 0.31 11
Mines 0.33 0.42 0.37 19
Pipes 0.00 0.00 0.00 17
Rockets 0.40 0.18 0.25 11
Vehicles 0.26 0.83 0.40 12
accuracy 0.31 70
macro avg 0.40 0.32 0.27 70
weighted avg 0.36 0.31 0.26 70
Training resnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6142557621002198
Epoch 2/10, Loss: 1.5696640729904174
Epoch 3/10, Loss: 1.734704566001892
Epoch 4/10, Loss: 1.839473056793213
Epoch 5/10, Loss: 1.676704740524292
Epoch 6/10, Loss: 1.6811943292617797
Epoch 7/10, Loss: 1.5289159059524535
Epoch 8/10, Loss: 1.5785106420516968
Epoch 9/10, Loss: 1.5617666482925414
Epoch 10/10, Loss: 1.5025474071502685
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.21 0.82 0.33 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.30 0.67 0.41 12
accuracy 0.24 70
macro avg 0.70 0.30 0.15 70
weighted avg 0.76 0.24 0.12 70
Training resnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.8113988161087036
Epoch 2/10, Loss: 1.6599361896514893
Epoch 3/10, Loss: 1.4571686744689942
Epoch 4/10, Loss: 1.2409714698791503
Epoch 5/10, Loss: 1.1503827333450318
Epoch 6/10, Loss: 1.1953710436820983
Epoch 7/10, Loss: 1.0146623134613038
Epoch 8/10, Loss: 1.017667543888092
Epoch 9/10, Loss: 0.9682670116424561
Epoch 10/10, Loss: 0.8932104229927063
Accuracy: 42.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.36 0.45 0.40 11
Mines 0.43 0.32 0.36 19
Pipes 0.86 0.35 0.50 17
Rockets 0.44 0.36 0.40 11
Vehicles 0.35 0.75 0.47 12
accuracy 0.43 70
macro avg 0.49 0.45 0.43 70
weighted avg 0.51 0.43 0.43 70
Training resnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7072265148162842
Epoch 2/10, Loss: 1.654572892189026
Epoch 3/10, Loss: 1.5619896173477172
Epoch 4/10, Loss: 1.5867202520370483
Epoch 5/10, Loss: 1.5287571668624877
Epoch 6/10, Loss: 1.4631927967071534
Epoch 7/10, Loss: 1.378524899482727
Epoch 8/10, Loss: 1.3465000867843628
Epoch 9/10, Loss: 1.338121509552002
Epoch 10/10, Loss: 1.3783397436141969
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.25 0.09 0.13 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.17 1.00 0.29 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.17 70
macro avg 0.68 0.22 0.08 70
weighted avg 0.75 0.17 0.07 70
Training resnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.913433837890625
Epoch 2/10, Loss: 1.7389235258102418
Epoch 3/10, Loss: 1.6530822038650512
Epoch 4/10, Loss: 1.4830502271652222
Epoch 5/10, Loss: 1.4718046188354492
Epoch 6/10, Loss: 1.3193755388259887
Epoch 7/10, Loss: 1.1953936576843263
Epoch 8/10, Loss: 1.185576581954956
Epoch 9/10, Loss: 1.0843329191207887
Epoch 10/10, Loss: 0.9523836493492126
Accuracy: 34.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.40 0.18 0.25 11
Mines 0.50 0.16 0.24 19
Pipes 0.30 0.47 0.36 17
Rockets 0.27 0.64 0.38 11
Vehicles 0.67 0.33 0.44 12
accuracy 0.34 70
macro avg 0.43 0.36 0.34 70
weighted avg 0.43 0.34 0.33 70
Training resnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.8498823642730713
Epoch 2/10, Loss: 1.712689709663391
Epoch 3/10, Loss: 1.4952569961547852
Epoch 4/10, Loss: 1.425341582298279
Epoch 5/10, Loss: 1.3417169809341432
Epoch 6/10, Loss: 1.1168644666671752
Epoch 7/10, Loss: 1.0896849036216736
Epoch 8/10, Loss: 1.0474685549736023
Epoch 9/10, Loss: 1.060193932056427
Epoch 10/10, Loss: 1.0930360078811645
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.08 0.27 0.12 11
Mines 0.00 0.00 0.00 19
Pipes 0.00 0.00 0.00 17
Rockets 1.00 0.09 0.17 11
Vehicles 0.23 0.58 0.33 12
accuracy 0.16 70
macro avg 0.26 0.19 0.12 70
weighted avg 0.21 0.16 0.10 70
Training resnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7548786878585816
Epoch 2/10, Loss: 1.70762300491333
Epoch 3/10, Loss: 1.5649228811264038
Epoch 4/10, Loss: 1.3412765979766845
Epoch 5/10, Loss: 1.3192384958267211
Epoch 6/10, Loss: 1.3277943134307861
Epoch 7/10, Loss: 1.4029572010040283
Epoch 8/10, Loss: 1.2456145286560059
Epoch 9/10, Loss: 1.186840867996216
Epoch 10/10, Loss: 1.1154050350189209
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.21 0.36 0.27 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.19 0.27 0.22 11
Vehicles 0.26 0.75 0.38 12
accuracy 0.23 70
macro avg 0.53 0.28 0.17 70
weighted avg 0.62 0.23 0.14 70
Training resnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 2.0212876081466673
Epoch 2/10, Loss: 2.0426632881164553
Epoch 3/10, Loss: 1.847366452217102
Epoch 4/10, Loss: 1.889521336555481
Epoch 5/10, Loss: 1.5252606630325318
Epoch 6/10, Loss: 1.5502670049667358
Epoch 7/10, Loss: 1.6814383506774901
Epoch 8/10, Loss: 1.678451919555664
Epoch 9/10, Loss: 1.6735010623931885
Epoch 10/10, Loss: 1.5717758893966676
Accuracy: 18.57%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.25 0.09 0.13 11
Vehicles 0.18 1.00 0.31 12
accuracy 0.19 70
macro avg 0.69 0.22 0.09 70
weighted avg 0.74 0.19 0.07 70
Training resnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.2936300767792597
Epoch 2/10, Loss: 0.6035107837782966
Epoch 3/10, Loss: 0.4513177014887333
Epoch 4/10, Loss: 0.2627859999322229
Epoch 5/10, Loss: 0.19526718013609448
Epoch 6/10, Loss: 0.24943131311900085
Epoch 7/10, Loss: 0.24210303897658983
Epoch 8/10, Loss: 0.14558827607995933
Epoch 9/10, Loss: 0.1443383905829655
Epoch 10/10, Loss: 0.09878164923025502
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.94 0.79 0.86 19
Pipes 0.94 1.00 0.97 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.89 70
macro avg 0.88 0.89 0.88 70
weighted avg 0.89 0.89 0.88 70
Training resnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.2829823195934296
Epoch 2/10, Loss: 0.49140265252855087
Epoch 3/10, Loss: 0.3083675019443035
Epoch 4/10, Loss: 0.40858522926767665
Epoch 5/10, Loss: 0.31505454145371914
Epoch 6/10, Loss: 0.24492033426132467
Epoch 7/10, Loss: 0.23976337112900284
Epoch 8/10, Loss: 0.14467980329775149
Epoch 9/10, Loss: 0.0813404354525523
Epoch 10/10, Loss: 0.0909091648645699
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 0.82 0.75 11
Mines 0.85 0.89 0.87 19
Pipes 0.94 0.94 0.94 17
Rockets 0.75 0.82 0.78 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.84 70
macro avg 0.85 0.83 0.83 70
weighted avg 0.86 0.84 0.84 70
Training resnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.3816672762235005
Epoch 2/10, Loss: 0.7019057207637363
Epoch 3/10, Loss: 0.5082119148638513
Epoch 4/10, Loss: 0.3644632192121612
Epoch 5/10, Loss: 0.24308507040970856
Epoch 6/10, Loss: 0.16731962623695532
Epoch 7/10, Loss: 0.2472841710680061
Epoch 8/10, Loss: 0.19413909357455042
Epoch 9/10, Loss: 0.13485709707149202
Epoch 10/10, Loss: 0.09144017493559255
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 1.00 0.89 0.94 19
Pipes 0.89 1.00 0.94 17
Rockets 0.90 0.82 0.86 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.90 70
macro avg 0.89 0.89 0.89 70
weighted avg 0.90 0.90 0.90 70
Training resnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.3025454878807068
Epoch 2/10, Loss: 0.5523444455530908
Epoch 3/10, Loss: 0.42686411138210034
Epoch 4/10, Loss: 0.18752613632629314
Epoch 5/10, Loss: 0.22360557752350965
Epoch 6/10, Loss: 0.3212108289202054
Epoch 7/10, Loss: 0.17469441440577307
Epoch 8/10, Loss: 0.13845577546291882
Epoch 9/10, Loss: 0.20632565466480124
Epoch 10/10, Loss: 0.2373987257273661
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.88 0.64 0.74 11
Mines 0.82 0.74 0.78 19
Pipes 0.92 0.71 0.80 17
Rockets 0.69 0.82 0.75 11
Vehicles 0.42 0.67 0.52 12
accuracy 0.71 70
macro avg 0.75 0.71 0.72 70
weighted avg 0.77 0.71 0.73 70
Training resnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3130158450868394
Epoch 2/10, Loss: 0.6123084111346139
Epoch 3/10, Loss: 0.4584528398182657
Epoch 4/10, Loss: 0.24672354219688308
Epoch 5/10, Loss: 0.2514868395196067
Epoch 6/10, Loss: 0.3007652933398883
Epoch 7/10, Loss: 0.2064160727378395
Epoch 8/10, Loss: 0.21576742796848217
Epoch 9/10, Loss: 0.21382927004661825
Epoch 10/10, Loss: 0.12495325474689405
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.82 0.95 0.88 19
Pipes 1.00 0.88 0.94 17
Rockets 0.90 0.82 0.86 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.89 70
macro avg 0.90 0.88 0.88 70
weighted avg 0.90 0.89 0.89 70
Training resnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.367995626396603
Epoch 2/10, Loss: 0.689593278699451
Epoch 3/10, Loss: 0.5790180663267771
Epoch 4/10, Loss: 0.24505152884456846
Epoch 5/10, Loss: 0.20096681960341004
Epoch 6/10, Loss: 0.1555762368047403
Epoch 7/10, Loss: 0.31839956653614837
Epoch 8/10, Loss: 0.18772401991817686
Epoch 9/10, Loss: 0.21450460909141433
Epoch 10/10, Loss: 0.06586666120630172
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.88 0.74 0.80 19
Pipes 0.88 0.88 0.88 17
Rockets 0.78 0.64 0.70 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.80 70
macro avg 0.79 0.80 0.79 70
weighted avg 0.81 0.80 0.80 70
Training resnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.2823876572979822
Epoch 2/10, Loss: 0.6972409900691774
Epoch 3/10, Loss: 0.3428086441838079
Epoch 4/10, Loss: 0.42630705299476784
Epoch 5/10, Loss: 0.2832650596068965
Epoch 6/10, Loss: 0.264558228974541
Epoch 7/10, Loss: 0.23658116058342987
Epoch 8/10, Loss: 0.18347861263383594
Epoch 9/10, Loss: 0.2083930206588573
Epoch 10/10, Loss: 0.19066945204718244
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.82 0.78 11
Mines 0.71 0.79 0.75 19
Pipes 0.94 0.94 0.94 17
Rockets 0.89 0.73 0.80 11
Vehicles 0.73 0.67 0.70 12
accuracy 0.80 70
macro avg 0.80 0.79 0.79 70
weighted avg 0.80 0.80 0.80 70
Training resnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.346183909310235
Epoch 2/10, Loss: 0.5799649059772491
Epoch 3/10, Loss: 0.4122610150112046
Epoch 4/10, Loss: 0.3147640625635783
Epoch 5/10, Loss: 0.24768363518847358
Epoch 6/10, Loss: 0.28052350889063543
Epoch 7/10, Loss: 0.28843262294928235
Epoch 8/10, Loss: 0.11773665870229404
Epoch 9/10, Loss: 0.07686403818014595
Epoch 10/10, Loss: 0.2309014883875433
Accuracy: 62.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.41 1.00 0.58 11
Mines 0.79 0.58 0.67 19
Pipes 0.83 0.59 0.69 17
Rockets 1.00 0.27 0.43 11
Vehicles 0.64 0.75 0.69 12
accuracy 0.63 70
macro avg 0.73 0.64 0.61 70
weighted avg 0.75 0.63 0.63 70
Training resnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3696296513080597
Epoch 2/10, Loss: 0.6461662501096725
Epoch 3/10, Loss: 0.4249504225121604
Epoch 4/10, Loss: 0.357512300213178
Epoch 5/10, Loss: 0.3348249060412248
Epoch 6/10, Loss: 0.22004887171917492
Epoch 7/10, Loss: 0.13533930594308508
Epoch 8/10, Loss: 0.15687780734151602
Epoch 9/10, Loss: 0.20191568534614313
Epoch 10/10, Loss: 0.12632787745032045
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.70 1.00 0.83 19
Pipes 0.94 1.00 0.97 17
Rockets 0.86 0.55 0.67 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.86 70
macro avg 0.90 0.82 0.84 70
weighted avg 0.88 0.86 0.85 70
Training resnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6009442210197449
Epoch 2/10, Loss: 1.554319269127316
Epoch 3/10, Loss: 1.4926750130123563
Epoch 4/10, Loss: 1.4362910919719272
Epoch 5/10, Loss: 1.3356392714712355
Epoch 6/10, Loss: 1.2379547953605652
Epoch 7/10, Loss: 1.1783624357647366
Epoch 8/10, Loss: 1.0693543487124972
Epoch 9/10, Loss: 0.9969180789258745
Epoch 10/10, Loss: 0.882725414302614
Accuracy: 65.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.45 0.91 0.61 11
Mines 0.83 0.53 0.65 19
Pipes 1.00 0.53 0.69 17
Rockets 0.53 0.73 0.62 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.66 70
macro avg 0.71 0.69 0.66 70
weighted avg 0.75 0.66 0.66 70
Training resnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.5886379414134555
Epoch 2/10, Loss: 1.545654747221205
Epoch 3/10, Loss: 1.4984671274820964
Epoch 4/10, Loss: 1.4636301067140367
Epoch 5/10, Loss: 1.399544682767656
Epoch 6/10, Loss: 1.3317851225535076
Epoch 7/10, Loss: 1.257342278957367
Epoch 8/10, Loss: 1.185728457238939
Epoch 9/10, Loss: 1.0588330560260348
Epoch 10/10, Loss: 0.9887263178825378
Accuracy: 61.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.55 1.00 0.71 11
Mines 0.91 0.53 0.67 19
Pipes 1.00 0.47 0.64 17
Rockets 0.57 0.36 0.44 11
Vehicles 0.42 0.83 0.56 12
accuracy 0.61 70
macro avg 0.69 0.64 0.60 70
weighted avg 0.74 0.61 0.61 70
Training resnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.618002090189192
Epoch 2/10, Loss: 1.576419989267985
Epoch 3/10, Loss: 1.52934248579873
Epoch 4/10, Loss: 1.4786426888571844
Epoch 5/10, Loss: 1.408474067846934
Epoch 6/10, Loss: 1.3695976932843525
Epoch 7/10, Loss: 1.2794917424519856
Epoch 8/10, Loss: 1.2524341146151226
Epoch 9/10, Loss: 1.1547187632984586
Epoch 10/10, Loss: 1.0396746893723805
Accuracy: 52.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.39 0.82 0.53 11
Mines 0.54 0.37 0.44 19
Pipes 1.00 0.47 0.64 17
Rockets 0.50 0.45 0.48 11
Vehicles 0.50 0.67 0.57 12
accuracy 0.53 70
macro avg 0.59 0.56 0.53 70
weighted avg 0.61 0.53 0.53 70
Training resnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6265477008289762
Epoch 2/10, Loss: 1.5776686933305528
Epoch 3/10, Loss: 1.521777802043491
Epoch 4/10, Loss: 1.4752945833735995
Epoch 5/10, Loss: 1.3915825419955783
Epoch 6/10, Loss: 1.3400020864274766
Epoch 7/10, Loss: 1.2699679997232225
Epoch 8/10, Loss: 1.206191407309638
Epoch 9/10, Loss: 1.0876045028368633
Epoch 10/10, Loss: 0.9908003045452966
Accuracy: 61.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.58 0.64 0.61 11
Mines 0.67 0.53 0.59 19
Pipes 0.90 0.53 0.67 17
Rockets 0.54 0.64 0.58 11
Vehicles 0.50 0.83 0.62 12
accuracy 0.61 70
macro avg 0.64 0.63 0.61 70
weighted avg 0.66 0.61 0.62 70
Training resnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6104957792494032
Epoch 2/10, Loss: 1.5576683613989089
Epoch 3/10, Loss: 1.5046772625711229
Epoch 4/10, Loss: 1.4486479229397244
Epoch 5/10, Loss: 1.3806231088108487
Epoch 6/10, Loss: 1.3163736595047846
Epoch 7/10, Loss: 1.2433222598499722
Epoch 8/10, Loss: 1.1412318348884583
Epoch 9/10, Loss: 1.0335712962680392
Epoch 10/10, Loss: 0.9693829384115007
Accuracy: 55.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.82 0.62 11
Mines 0.53 0.42 0.47 19
Pipes 1.00 0.35 0.52 17
Rockets 0.39 0.64 0.48 11
Vehicles 0.69 0.75 0.72 12
accuracy 0.56 70
macro avg 0.62 0.60 0.56 70
weighted avg 0.65 0.56 0.55 70
Training resnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6226515372594197
Epoch 2/10, Loss: 1.5747153626547918
Epoch 3/10, Loss: 1.5334792137145996
Epoch 4/10, Loss: 1.4900752703348796
Epoch 5/10, Loss: 1.4357710017098322
Epoch 6/10, Loss: 1.3710951738887363
Epoch 7/10, Loss: 1.2869076530138652
Epoch 8/10, Loss: 1.204351352320777
Epoch 9/10, Loss: 1.1420910159746807
Epoch 10/10, Loss: 1.0531707869635687
Accuracy: 67.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.45 0.91 0.61 11
Mines 0.82 0.47 0.60 19
Pipes 0.92 0.65 0.76 17
Rockets 0.70 0.64 0.67 11
Vehicles 0.67 0.83 0.74 12
accuracy 0.67 70
macro avg 0.71 0.70 0.67 70
weighted avg 0.74 0.67 0.67 70
Training resnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5985904004838731
Epoch 2/10, Loss: 1.5578024519814386
Epoch 3/10, Loss: 1.501756681336297
Epoch 4/10, Loss: 1.4498055179913838
Epoch 5/10, Loss: 1.3827771213319566
Epoch 6/10, Loss: 1.3038273321257696
Epoch 7/10, Loss: 1.217517269982232
Epoch 8/10, Loss: 1.134654912683699
Epoch 9/10, Loss: 1.0369612707032099
Epoch 10/10, Loss: 0.9515979654259152
Accuracy: 60.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.53 0.91 0.67 11
Mines 0.63 0.63 0.63 19
Pipes 0.70 0.41 0.52 17
Rockets 0.46 0.55 0.50 11
Vehicles 0.78 0.58 0.67 12
accuracy 0.60 70
macro avg 0.62 0.62 0.60 70
weighted avg 0.63 0.60 0.59 70
Training resnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5981748898824055
Epoch 2/10, Loss: 1.566383421421051
Epoch 3/10, Loss: 1.52183018790351
Epoch 4/10, Loss: 1.4689236481984456
Epoch 5/10, Loss: 1.4317969547377691
Epoch 6/10, Loss: 1.3510952591896057
Epoch 7/10, Loss: 1.270302904976739
Epoch 8/10, Loss: 1.1969735622406006
Epoch 9/10, Loss: 1.0925094584623973
Epoch 10/10, Loss: 1.0038219723436568
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.53 0.82 0.64 11
Mines 0.86 0.63 0.73 19
Pipes 0.93 0.82 0.88 17
Rockets 0.62 0.45 0.53 11
Vehicles 0.62 0.83 0.71 12
accuracy 0.71 70
macro avg 0.71 0.71 0.70 70
weighted avg 0.75 0.71 0.72 70
Training resnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6247135268317328
Epoch 2/10, Loss: 1.564759612083435
Epoch 3/10, Loss: 1.5019287003411188
Epoch 4/10, Loss: 1.4801636470688715
Epoch 5/10, Loss: 1.4411094983418782
Epoch 6/10, Loss: 1.3949020703633626
Epoch 7/10, Loss: 1.3116345405578613
Epoch 8/10, Loss: 1.233598166041904
Epoch 9/10, Loss: 1.132914533217748
Epoch 10/10, Loss: 1.0471551915009816
Accuracy: 61.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.91 0.69 11
Mines 0.56 0.47 0.51 19
Pipes 1.00 0.53 0.69 17
Rockets 0.50 0.64 0.56 11
Vehicles 0.62 0.67 0.64 12
accuracy 0.61 70
macro avg 0.65 0.64 0.62 70
weighted avg 0.67 0.61 0.61 70
Training resnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.593191961447398
Epoch 2/10, Loss: 1.3453067077530756
Epoch 3/10, Loss: 1.1996384461720784
Epoch 4/10, Loss: 1.1667808327409956
Epoch 5/10, Loss: 0.8616621676418517
Epoch 6/10, Loss: 0.8927401287688149
Epoch 7/10, Loss: 0.6584968848360909
Epoch 8/10, Loss: 0.5649245588315858
Epoch 9/10, Loss: 0.6413653476370705
Epoch 10/10, Loss: 0.5780098372035556
Accuracy: 35.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.40 0.18 0.25 11
Mines 0.30 0.32 0.31 19
Pipes 1.00 0.12 0.21 17
Rockets 0.71 0.45 0.56 11
Vehicles 0.28 0.83 0.42 12
accuracy 0.36 70
macro avg 0.54 0.38 0.35 70
weighted avg 0.55 0.36 0.33 70
Training resnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.5721762445237901
Epoch 2/10, Loss: 1.3839063478840723
Epoch 3/10, Loss: 1.151079257329305
Epoch 4/10, Loss: 1.0404442979229822
Epoch 5/10, Loss: 0.862252758608924
Epoch 6/10, Loss: 0.8370038337177701
Epoch 7/10, Loss: 0.6002344903018739
Epoch 8/10, Loss: 0.47067075388299096
Epoch 9/10, Loss: 0.4427915041645368
Epoch 10/10, Loss: 0.6141683393054538
Accuracy: 58.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.46 0.55 0.50 11
Mines 0.61 0.89 0.72 19
Pipes 0.58 0.82 0.68 17
Rockets 0.67 0.18 0.29 11
Vehicles 1.00 0.17 0.29 12
accuracy 0.59 70
macro avg 0.66 0.52 0.50 70
weighted avg 0.66 0.59 0.53 70
Training resnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.648085508081648
Epoch 2/10, Loss: 1.4761022528012593
Epoch 3/10, Loss: 1.3924771083725824
Epoch 4/10, Loss: 1.2921517425113254
Epoch 5/10, Loss: 1.1983268625206418
Epoch 6/10, Loss: 1.127453886800342
Epoch 7/10, Loss: 0.8803981277677748
Epoch 8/10, Loss: 0.8147076699468825
Epoch 9/10, Loss: 0.6086856234404776
Epoch 10/10, Loss: 0.6219479276074303
Accuracy: 58.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.64 0.64 11
Mines 0.43 0.84 0.57 19
Pipes 1.00 0.24 0.38 17
Rockets 0.62 0.45 0.53 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.59 70
macro avg 0.72 0.58 0.59 70
weighted avg 0.71 0.59 0.57 70
Training resnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6410976714558072
Epoch 2/10, Loss: 1.2779170076052349
Epoch 3/10, Loss: 1.0115559366014268
Epoch 4/10, Loss: 0.8413756291071574
Epoch 5/10, Loss: 0.6636462112267812
Epoch 6/10, Loss: 0.6020842525694106
Epoch 7/10, Loss: 0.5861137898431884
Epoch 8/10, Loss: 0.39348555439048344
Epoch 9/10, Loss: 0.3817281449834506
Epoch 10/10, Loss: 0.5329083046979375
Accuracy: 72.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.82 0.72 11
Mines 0.81 0.68 0.74 19
Pipes 0.62 0.88 0.73 17
Rockets 1.00 0.45 0.62 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.73 70
macro avg 0.78 0.72 0.72 70
weighted avg 0.77 0.73 0.72 70
Training resnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6523339417245653
Epoch 2/10, Loss: 1.4675677220026653
Epoch 3/10, Loss: 1.1441576447751787
Epoch 4/10, Loss: 1.1272129681375291
Epoch 5/10, Loss: 0.9128292997678121
Epoch 6/10, Loss: 0.7792309059037102
Epoch 7/10, Loss: 0.9218207001686096
Epoch 8/10, Loss: 0.6958620333009295
Epoch 9/10, Loss: 0.605860764781634
Epoch 10/10, Loss: 0.6608752111593882
Accuracy: 48.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.45 0.59 11
Mines 0.60 0.16 0.25 19
Pipes 0.71 0.71 0.71 17
Rockets 0.24 0.82 0.38 11
Vehicles 1.00 0.42 0.59 12
accuracy 0.49 70
macro avg 0.68 0.51 0.50 70
weighted avg 0.67 0.49 0.49 70
Training resnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5550198091400995
Epoch 2/10, Loss: 1.4184272355503507
Epoch 3/10, Loss: 1.2487090395556555
Epoch 4/10, Loss: 1.219045811229282
Epoch 5/10, Loss: 0.8887478411197662
Epoch 6/10, Loss: 0.9306222630871667
Epoch 7/10, Loss: 0.7173902773194842
Epoch 8/10, Loss: 0.6337456984652413
Epoch 9/10, Loss: 0.46752870082855225
Epoch 10/10, Loss: 0.46646663587954307
Accuracy: 60.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.47 0.73 0.57 11
Mines 0.57 0.21 0.31 19
Pipes 0.83 0.88 0.86 17
Rockets 0.75 0.55 0.63 11
Vehicles 0.45 0.75 0.56 12
accuracy 0.60 70
macro avg 0.62 0.62 0.59 70
weighted avg 0.63 0.60 0.58 70
Training resnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6146692832310994
Epoch 2/10, Loss: 1.2808685766326056
Epoch 3/10, Loss: 1.1814409295717876
Epoch 4/10, Loss: 1.0482279790772333
Epoch 5/10, Loss: 0.9025622142685784
Epoch 6/10, Loss: 0.8511500342024697
Epoch 7/10, Loss: 0.5843269593185849
Epoch 8/10, Loss: 0.6981944640477499
Epoch 9/10, Loss: 0.5290181868606143
Epoch 10/10, Loss: 0.4913019546204143
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.82 0.72 11
Mines 0.71 0.79 0.75 19
Pipes 1.00 0.71 0.83 17
Rockets 0.67 0.91 0.77 11
Vehicles 0.88 0.58 0.70 12
accuracy 0.76 70
macro avg 0.78 0.76 0.75 70
weighted avg 0.79 0.76 0.76 70
Training resnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6344122224383884
Epoch 2/10, Loss: 1.508107860883077
Epoch 3/10, Loss: 1.2153318723042805
Epoch 4/10, Loss: 1.0615494814183977
Epoch 5/10, Loss: 1.0918270150820415
Epoch 6/10, Loss: 0.9225960638788011
Epoch 7/10, Loss: 0.971726291709476
Epoch 8/10, Loss: 0.6839762611521615
Epoch 9/10, Loss: 0.6333556820948919
Epoch 10/10, Loss: 0.5644430733389325
Accuracy: 38.57%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.18 0.31 11
Mines 0.50 0.11 0.17 19
Pipes 1.00 0.47 0.64 17
Rockets 0.44 0.36 0.40 11
Vehicles 0.23 0.92 0.37 12
accuracy 0.39 70
macro avg 0.64 0.41 0.38 70
weighted avg 0.65 0.39 0.38 70
Training resnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.672783546977573
Epoch 2/10, Loss: 1.3859819769859314
Epoch 3/10, Loss: 1.3079372313287523
Epoch 4/10, Loss: 1.2363561987876892
Epoch 5/10, Loss: 1.267857978741328
Epoch 6/10, Loss: 1.0956280761294894
Epoch 7/10, Loss: 1.1824562085999384
Epoch 8/10, Loss: 1.1336672008037567
Epoch 9/10, Loss: 1.087207403447893
Epoch 10/10, Loss: 1.018911576933331
Accuracy: 44.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.55 0.60 11
Mines 0.56 0.26 0.36 19
Pipes 0.67 0.35 0.46 17
Rockets 0.25 0.45 0.32 11
Vehicles 0.39 0.75 0.51 12
accuracy 0.44 70
macro avg 0.51 0.47 0.45 70
weighted avg 0.52 0.44 0.44 70
Training resnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.2869570917553372
Epoch 2/10, Loss: 0.4861434929900699
Epoch 3/10, Loss: 0.16895431280136108
Epoch 4/10, Loss: 0.12600200571533707
Epoch 5/10, Loss: 0.1138886438889636
Epoch 6/10, Loss: 0.162289218356212
Epoch 7/10, Loss: 0.06897789840069082
Epoch 8/10, Loss: 0.02412983061124881
Epoch 9/10, Loss: 0.021913247959067423
Epoch 10/10, Loss: 0.04397765536689096
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.89 0.84 0.86 19
Pipes 1.00 0.94 0.97 17
Rockets 1.00 1.00 1.00 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.91 70
macro avg 0.92 0.92 0.92 70
weighted avg 0.93 0.91 0.92 70
Training resnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.3917702436447144
Epoch 2/10, Loss: 0.5374015867710114
Epoch 3/10, Loss: 0.19513606114519966
Epoch 4/10, Loss: 0.1473509988023175
Epoch 5/10, Loss: 0.2205665343337589
Epoch 6/10, Loss: 0.2523182953397433
Epoch 7/10, Loss: 0.11869524046778679
Epoch 8/10, Loss: 0.09480746328416798
Epoch 9/10, Loss: 0.029153621859020658
Epoch 10/10, Loss: 0.03788496328828236
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.94 0.84 0.89 19
Pipes 1.00 0.82 0.90 17
Rockets 0.71 0.91 0.80 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.84 70
macro avg 0.84 0.85 0.84 70
weighted avg 0.87 0.84 0.85 70
Training resnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.4661134746339586
Epoch 2/10, Loss: 0.765986829996109
Epoch 3/10, Loss: 0.2796482940514882
Epoch 4/10, Loss: 0.16083003332217535
Epoch 5/10, Loss: 0.08569707121286127
Epoch 6/10, Loss: 0.04790319057388438
Epoch 7/10, Loss: 0.1939288828935888
Epoch 8/10, Loss: 0.1873923552532991
Epoch 9/10, Loss: 0.12005591599477662
Epoch 10/10, Loss: 0.14112187176942825
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.91 0.65 11
Mines 1.00 0.79 0.88 19
Pipes 0.93 0.76 0.84 17
Rockets 0.82 0.82 0.82 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.81 70
macro avg 0.85 0.82 0.82 70
weighted avg 0.88 0.81 0.83 70
Training resnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.3553386529286702
Epoch 2/10, Loss: 0.47628504534562427
Epoch 3/10, Loss: 0.14189559717973074
Epoch 4/10, Loss: 0.08585608812669913
Epoch 5/10, Loss: 0.13089053850207064
Epoch 6/10, Loss: 0.21391511377361086
Epoch 7/10, Loss: 0.11697381776240137
Epoch 8/10, Loss: 0.08504248679512078
Epoch 9/10, Loss: 0.05144029938512378
Epoch 10/10, Loss: 0.02079457908661829
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.83 0.79 0.81 19
Pipes 0.88 0.82 0.85 17
Rockets 0.64 0.82 0.72 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.81 70
macro avg 0.82 0.82 0.81 70
weighted avg 0.83 0.81 0.82 70
Training resnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.4389707644780476
Epoch 2/10, Loss: 0.6280839575661553
Epoch 3/10, Loss: 0.2874600862463315
Epoch 4/10, Loss: 0.1596306934952736
Epoch 5/10, Loss: 0.12532012392249373
Epoch 6/10, Loss: 0.15866236430075434
Epoch 7/10, Loss: 0.12641451973468065
Epoch 8/10, Loss: 0.15679587175448736
Epoch 9/10, Loss: 0.18693175042668977
Epoch 10/10, Loss: 0.19258340365356869
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.54 0.79 0.64 19
Pipes 1.00 0.59 0.74 17
Rockets 0.73 0.73 0.73 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.71 70
macro avg 0.80 0.72 0.73 70
weighted avg 0.79 0.71 0.72 70
Training resnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.4607531627019246
Epoch 2/10, Loss: 0.7263506485356225
Epoch 3/10, Loss: 0.2529267701837752
Epoch 4/10, Loss: 0.1230240532507499
Epoch 5/10, Loss: 0.12723822788231903
Epoch 6/10, Loss: 0.16203460221489271
Epoch 7/10, Loss: 0.09154102371798621
Epoch 8/10, Loss: 0.0391229379715191
Epoch 9/10, Loss: 0.04682910271609823
Epoch 10/10, Loss: 0.08612225535843107
Accuracy: 65.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.80 0.36 0.50 11
Mines 0.62 0.79 0.70 19
Pipes 1.00 0.71 0.83 17
Rockets 0.62 0.45 0.53 11
Vehicles 0.48 0.83 0.61 12
accuracy 0.66 70
macro avg 0.71 0.63 0.63 70
weighted avg 0.72 0.66 0.66 70
Training resnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.3632987605200872
Epoch 2/10, Loss: 0.5512508816189237
Epoch 3/10, Loss: 0.15126140084531572
Epoch 4/10, Loss: 0.13287715572449896
Epoch 5/10, Loss: 0.1789870055185424
Epoch 6/10, Loss: 0.11278259505828221
Epoch 7/10, Loss: 0.0788166800306903
Epoch 8/10, Loss: 0.07496818858716223
Epoch 9/10, Loss: 0.039159072356091604
Epoch 10/10, Loss: 0.05069705538658632
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.85 0.89 0.87 19
Pipes 1.00 0.88 0.94 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.50 0.67 12
accuracy 0.86 70
macro avg 0.87 0.86 0.84 70
weighted avg 0.88 0.86 0.85 70
Training resnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.4609247048695881
Epoch 2/10, Loss: 0.6665536363919576
Epoch 3/10, Loss: 0.1893517565396097
Epoch 4/10, Loss: 0.13294661665956178
Epoch 5/10, Loss: 0.09842811980181271
Epoch 6/10, Loss: 0.16374465616212952
Epoch 7/10, Loss: 0.19341693653000724
Epoch 8/10, Loss: 0.08022019722395474
Epoch 9/10, Loss: 0.04265758477979236
Epoch 10/10, Loss: 0.020524701258788507
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.82 0.82 0.82 11
Mines 0.86 0.95 0.90 19
Pipes 0.94 0.88 0.91 17
Rockets 0.71 0.91 0.80 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.86 70
macro avg 0.87 0.84 0.85 70
weighted avg 0.87 0.86 0.86 70
Training resnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.403526054488288
Epoch 2/10, Loss: 0.6840909322102865
Epoch 3/10, Loss: 0.29930386112795937
Epoch 4/10, Loss: 0.0930115237004227
Epoch 5/10, Loss: 0.06355037405673
Epoch 6/10, Loss: 0.26240259326166576
Epoch 7/10, Loss: 0.29752209617031944
Epoch 8/10, Loss: 0.39806048489279217
Epoch 9/10, Loss: 0.17986165318224165
Epoch 10/10, Loss: 0.1480377440651258
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.86 0.95 0.90 19
Pipes 0.93 0.76 0.84 17
Rockets 0.77 0.91 0.83 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.86 70
macro avg 0.86 0.86 0.85 70
weighted avg 0.86 0.86 0.86 70
Training resnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6191072993808322
Epoch 2/10, Loss: 1.596831546889411
Epoch 3/10, Loss: 1.5705480575561523
Epoch 4/10, Loss: 1.5384234719806247
Epoch 5/10, Loss: 1.5048645734786987
Epoch 6/10, Loss: 1.474389420615302
Epoch 7/10, Loss: 1.4420556492275662
Epoch 8/10, Loss: 1.3980398178100586
Epoch 9/10, Loss: 1.3580414189232721
Epoch 10/10, Loss: 1.321777833832635
Accuracy: 42.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.62 0.73 0.67 11
Mines 0.46 0.32 0.38 19
Pipes 1.00 0.24 0.38 17
Rockets 0.40 0.18 0.25 11
Vehicles 0.29 0.83 0.43 12
accuracy 0.43 70
macro avg 0.55 0.46 0.42 70
weighted avg 0.58 0.43 0.41 70
Training resnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6071778138478596
Epoch 2/10, Loss: 1.5836612118615045
Epoch 3/10, Loss: 1.5532580084270902
Epoch 4/10, Loss: 1.5322212510638766
Epoch 5/10, Loss: 1.507804685168796
Epoch 6/10, Loss: 1.4914629724290636
Epoch 7/10, Loss: 1.4508763154347737
Epoch 8/10, Loss: 1.4053583410051134
Epoch 9/10, Loss: 1.3725766605801053
Epoch 10/10, Loss: 1.3317769633399115
Accuracy: 32.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.62 0.45 0.53 11
Mines 0.33 0.05 0.09 19
Pipes 1.00 0.18 0.30 17
Rockets 0.50 0.27 0.35 11
Vehicles 0.22 0.92 0.35 12
accuracy 0.33 70
macro avg 0.54 0.37 0.33 70
weighted avg 0.55 0.33 0.30 70
Training resnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6037140687306721
Epoch 2/10, Loss: 1.5863338046603732
Epoch 3/10, Loss: 1.5489085912704468
Epoch 4/10, Loss: 1.536415974299113
Epoch 5/10, Loss: 1.4952814843919542
Epoch 6/10, Loss: 1.45068977938758
Epoch 7/10, Loss: 1.4178448518117268
Epoch 8/10, Loss: 1.3784056239657931
Epoch 9/10, Loss: 1.3311073647605047
Epoch 10/10, Loss: 1.2555912997987535
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.46 0.55 0.50 11
Mines 0.50 0.11 0.17 19
Pipes 1.00 0.24 0.38 17
Rockets 0.40 0.36 0.38 11
Vehicles 0.26 0.83 0.39 12
accuracy 0.37 70
macro avg 0.52 0.42 0.37 70
weighted avg 0.56 0.37 0.35 70
Training resnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6121052503585815
Epoch 2/10, Loss: 1.5932339032491047
Epoch 3/10, Loss: 1.5654682848188612
Epoch 4/10, Loss: 1.532524002922906
Epoch 5/10, Loss: 1.4980199999279447
Epoch 6/10, Loss: 1.4632459349102445
Epoch 7/10, Loss: 1.4060067998038397
Epoch 8/10, Loss: 1.3694468206829495
Epoch 9/10, Loss: 1.3241411050160725
Epoch 10/10, Loss: 1.2936471568213568
Accuracy: 34.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.46 0.55 0.50 11
Mines 1.00 0.11 0.19 19
Pipes 1.00 0.06 0.11 17
Rockets 0.38 0.45 0.42 11
Vehicles 0.24 0.83 0.38 12
accuracy 0.34 70
macro avg 0.62 0.40 0.32 70
weighted avg 0.69 0.34 0.29 70
Training resnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.619270430670844
Epoch 2/10, Loss: 1.6052456167009141
Epoch 3/10, Loss: 1.5661725070741441
Epoch 4/10, Loss: 1.551913234922621
Epoch 5/10, Loss: 1.5161990192201402
Epoch 6/10, Loss: 1.4815001090367634
Epoch 7/10, Loss: 1.4388292630513508
Epoch 8/10, Loss: 1.4086150858137343
Epoch 9/10, Loss: 1.3894305891460843
Epoch 10/10, Loss: 1.328805963198344
Accuracy: 40.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.23 0.27 0.25 11
Mines 0.67 0.21 0.32 19
Pipes 1.00 0.29 0.45 17
Rockets 0.40 0.55 0.46 11
Vehicles 0.32 0.83 0.47 12
accuracy 0.40 70
macro avg 0.52 0.43 0.39 70
weighted avg 0.58 0.40 0.39 70
Training resnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.596311993069119
Epoch 2/10, Loss: 1.5707791911231146
Epoch 3/10, Loss: 1.5396066771613226
Epoch 4/10, Loss: 1.5107424789004855
Epoch 5/10, Loss: 1.4758552180396185
Epoch 6/10, Loss: 1.4310896396636963
Epoch 7/10, Loss: 1.3973423110114203
Epoch 8/10, Loss: 1.332213216357761
Epoch 9/10, Loss: 1.3080924881829157
Epoch 10/10, Loss: 1.2549716101752386
Accuracy: 32.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.62 0.45 0.53 11
Mines 0.00 0.00 0.00 19
Pipes 1.00 0.12 0.21 17
Rockets 0.45 0.45 0.45 11
Vehicles 0.23 0.92 0.37 12
accuracy 0.33 70
macro avg 0.46 0.39 0.31 70
weighted avg 0.45 0.33 0.27 70
Training resnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6019409232669406
Epoch 2/10, Loss: 1.579379849963718
Epoch 3/10, Loss: 1.5509170161353216
Epoch 4/10, Loss: 1.5176223781373765
Epoch 5/10, Loss: 1.4759003851148818
Epoch 6/10, Loss: 1.435515324274699
Epoch 7/10, Loss: 1.385808573828803
Epoch 8/10, Loss: 1.334229760699802
Epoch 9/10, Loss: 1.2955944538116455
Epoch 10/10, Loss: 1.2256869739956326
Accuracy: 44.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.82 0.67 11
Mines 0.67 0.21 0.32 19
Pipes 0.83 0.29 0.43 17
Rockets 0.31 0.36 0.33 11
Vehicles 0.31 0.75 0.44 12
accuracy 0.44 70
macro avg 0.54 0.49 0.44 70
weighted avg 0.57 0.44 0.42 70
Training resnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6012074020173814
Epoch 2/10, Loss: 1.5984109905030992
Epoch 3/10, Loss: 1.566028012169732
Epoch 4/10, Loss: 1.5375961197747126
Epoch 5/10, Loss: 1.505142370859782
Epoch 6/10, Loss: 1.4736527469423082
Epoch 7/10, Loss: 1.4446426894929674
Epoch 8/10, Loss: 1.3927368058098688
Epoch 9/10, Loss: 1.3740443256166246
Epoch 10/10, Loss: 1.307745271258884
Accuracy: 54.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.53 0.73 0.62 11
Mines 0.91 0.53 0.67 19
Pipes 1.00 0.29 0.45 17
Rockets 0.57 0.36 0.44 11
Vehicles 0.34 0.92 0.50 12
accuracy 0.54 70
macro avg 0.67 0.57 0.54 70
weighted avg 0.72 0.54 0.54 70
Training resnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6284308036168416
Epoch 2/10, Loss: 1.6173282199435763
Epoch 3/10, Loss: 1.5689178440305922
Epoch 4/10, Loss: 1.5506100522147284
Epoch 5/10, Loss: 1.5114222764968872
Epoch 6/10, Loss: 1.4787027968300714
Epoch 7/10, Loss: 1.4334947930441961
Epoch 8/10, Loss: 1.4281113942464192
Epoch 9/10, Loss: 1.3908230066299438
Epoch 10/10, Loss: 1.3501713540818956
Accuracy: 38.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.42 0.73 0.53 11
Mines 0.33 0.05 0.09 19
Pipes 1.00 0.18 0.30 17
Rockets 0.44 0.36 0.40 11
Vehicles 0.31 0.92 0.46 12
accuracy 0.39 70
macro avg 0.50 0.45 0.36 70
weighted avg 0.52 0.39 0.32 70
Training resnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.5444249047173395
Epoch 2/10, Loss: 1.4338665405909221
Epoch 3/10, Loss: 1.2184807128376431
Epoch 4/10, Loss: 1.0059556232558355
Epoch 5/10, Loss: 0.9127362569173177
Epoch 6/10, Loss: 0.7631372809410095
Epoch 7/10, Loss: 0.5156804290082719
Epoch 8/10, Loss: 0.6305595338344574
Epoch 9/10, Loss: 0.41730524102846783
Epoch 10/10, Loss: 0.526747528049681
Accuracy: 64.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.62 0.45 0.53 11
Mines 0.92 0.58 0.71 19
Pipes 0.91 0.59 0.71 17
Rockets 0.52 1.00 0.69 11
Vehicles 0.44 0.67 0.53 12
accuracy 0.64 70
macro avg 0.68 0.66 0.63 70
weighted avg 0.73 0.64 0.65 70
Training resnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6945516268412273
Epoch 2/10, Loss: 1.4251162078645494
Epoch 3/10, Loss: 1.310670985115899
Epoch 4/10, Loss: 1.2014902962578669
Epoch 5/10, Loss: 1.1004554828008015
Epoch 6/10, Loss: 0.9310050739182366
Epoch 7/10, Loss: 0.9108930230140686
Epoch 8/10, Loss: 0.6826468374994066
Epoch 9/10, Loss: 0.6370708578162723
Epoch 10/10, Loss: 0.3787195533514023
Accuracy: 51.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.36 0.47 11
Mines 1.00 0.21 0.35 19
Pipes 1.00 0.47 0.64 17
Rockets 0.48 1.00 0.65 11
Vehicles 0.31 0.75 0.44 12
accuracy 0.51 70
macro avg 0.69 0.56 0.51 70
weighted avg 0.75 0.51 0.50 70
Training resnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 2.19171581003401
Epoch 2/10, Loss: 1.4960213899612427
Epoch 3/10, Loss: 1.226412958568997
Epoch 4/10, Loss: 0.9813772969775729
Epoch 5/10, Loss: 0.7768729858928256
Epoch 6/10, Loss: 0.6628415849473741
Epoch 7/10, Loss: 0.5289619995488061
Epoch 8/10, Loss: 0.5064152247375913
Epoch 9/10, Loss: 0.3291233744886186
Epoch 10/10, Loss: 0.2911147326231003
Accuracy: 44.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.26 0.91 0.41 11
Mines 0.75 0.16 0.26 19
Pipes 1.00 0.29 0.45 17
Rockets 0.40 0.55 0.46 11
Vehicles 0.88 0.58 0.70 12
accuracy 0.44 70
macro avg 0.66 0.50 0.46 70
weighted avg 0.70 0.44 0.44 70
Training resnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5878717766867743
Epoch 2/10, Loss: 1.4364666673872206
Epoch 3/10, Loss: 1.1660173270437453
Epoch 4/10, Loss: 1.0548704266548157
Epoch 5/10, Loss: 0.7075840996371375
Epoch 6/10, Loss: 0.6626991298463609
Epoch 7/10, Loss: 0.4537813067436218
Epoch 8/10, Loss: 0.47632798221376205
Epoch 9/10, Loss: 0.4640556772549947
Epoch 10/10, Loss: 0.3958219786485036
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.80 0.73 0.76 11
Mines 0.80 0.84 0.82 19
Pipes 0.70 0.82 0.76 17
Rockets 0.58 0.64 0.61 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.76 70
macro avg 0.78 0.74 0.75 70
weighted avg 0.78 0.76 0.76 70
Training resnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.660747037993537
Epoch 2/10, Loss: 1.3772518171204462
Epoch 3/10, Loss: 0.9608100983831618
Epoch 4/10, Loss: 0.7023210591740079
Epoch 5/10, Loss: 0.6063111623128256
Epoch 6/10, Loss: 0.4949923786852095
Epoch 7/10, Loss: 0.29915404154194725
Epoch 8/10, Loss: 0.16985367611050606
Epoch 9/10, Loss: 0.3219817711247338
Epoch 10/10, Loss: 0.14031998647583854
Accuracy: 72.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.59 0.91 0.71 11
Mines 0.58 0.74 0.65 19
Pipes 1.00 0.53 0.69 17
Rockets 0.89 0.73 0.80 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.73 70
macro avg 0.79 0.75 0.75 70
weighted avg 0.79 0.73 0.73 70
Training resnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6387830972671509
Epoch 2/10, Loss: 1.258979366885291
Epoch 3/10, Loss: 1.0421229600906372
Epoch 4/10, Loss: 0.7253762582937876
Epoch 5/10, Loss: 0.6248235238922967
Epoch 6/10, Loss: 0.3664536161555184
Epoch 7/10, Loss: 0.2677222639322281
Epoch 8/10, Loss: 0.32510239217016434
Epoch 9/10, Loss: 0.1361155944565932
Epoch 10/10, Loss: 0.09460173919796944
Accuracy: 64.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.80 0.36 0.50 11
Mines 0.46 0.95 0.62 19
Pipes 1.00 0.41 0.58 17
Rockets 0.67 0.55 0.60 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.64 70
macro avg 0.79 0.62 0.64 70
weighted avg 0.77 0.64 0.64 70
Training resnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5722847912046645
Epoch 2/10, Loss: 1.3838003608915541
Epoch 3/10, Loss: 1.3837054371833801
Epoch 4/10, Loss: 0.9811287720998129
Epoch 5/10, Loss: 0.9246085219913058
Epoch 6/10, Loss: 0.7661118374930488
Epoch 7/10, Loss: 0.7518129746119181
Epoch 8/10, Loss: 0.6069667869144015
Epoch 9/10, Loss: 0.5300260086854299
Epoch 10/10, Loss: 0.5900011459986368
Accuracy: 55.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.55 0.63 11
Mines 0.40 0.89 0.56 19
Pipes 0.67 0.24 0.35 17
Rockets 0.60 0.27 0.38 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.56 70
macro avg 0.68 0.54 0.55 70
weighted avg 0.66 0.56 0.54 70
Training resnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6711270146899753
Epoch 2/10, Loss: 1.5463124910990398
Epoch 3/10, Loss: 1.5513170560201008
Epoch 4/10, Loss: 1.1704037454393175
Epoch 5/10, Loss: 1.0526776711146038
Epoch 6/10, Loss: 0.6916599406136407
Epoch 7/10, Loss: 0.6682806511720022
Epoch 8/10, Loss: 0.4784037205908034
Epoch 9/10, Loss: 0.3536514970991347
Epoch 10/10, Loss: 0.6484601497650146
Accuracy: 68.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.82 0.67 11
Mines 0.68 0.68 0.68 19
Pipes 0.71 0.59 0.65 17
Rockets 0.64 0.82 0.72 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.69 70
macro avg 0.72 0.70 0.69 70
weighted avg 0.72 0.69 0.69 70
Training resnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6380460659662883
Epoch 2/10, Loss: 1.311637110180325
Epoch 3/10, Loss: 1.4382617606057062
Epoch 4/10, Loss: 1.2911675771077473
Epoch 5/10, Loss: 1.0388180547290378
Epoch 6/10, Loss: 0.8119998110665215
Epoch 7/10, Loss: 0.7847874429490831
Epoch 8/10, Loss: 0.5981232093440162
Epoch 9/10, Loss: 0.47338391012615627
Epoch 10/10, Loss: 0.4454152153597938
Accuracy: 57.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.78 0.64 0.70 11
Mines 0.67 0.32 0.43 19
Pipes 0.69 0.65 0.67 17
Rockets 0.36 0.82 0.50 11
Vehicles 0.64 0.58 0.61 12
accuracy 0.57 70
macro avg 0.63 0.60 0.58 70
weighted avg 0.64 0.57 0.57 70
Training resnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.4505961894989015
Epoch 2/10, Loss: 0.7001823306083679
Epoch 3/10, Loss: 0.19844264686107635
Epoch 4/10, Loss: 0.045520104467868805
Epoch 5/10, Loss: 0.014366636611521244
Epoch 6/10, Loss: 0.016249480098485945
Epoch 7/10, Loss: 0.006589450314640999
Epoch 8/10, Loss: 0.027583510987460612
Epoch 9/10, Loss: 0.08750480706803501
Epoch 10/10, Loss: 0.14583713188767433
Accuracy: 64.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.45 0.62 11
Mines 0.71 0.63 0.67 19
Pipes 1.00 0.65 0.79 17
Rockets 0.89 0.73 0.80 11
Vehicles 0.32 0.75 0.45 12
accuracy 0.64 70
macro avg 0.78 0.64 0.67 70
weighted avg 0.79 0.64 0.67 70
Training resnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.4638690948486328
Epoch 2/10, Loss: 0.7970317602157593
Epoch 3/10, Loss: 0.2061735972762108
Epoch 4/10, Loss: 0.09141872227191924
Epoch 5/10, Loss: 0.060566619783639905
Epoch 6/10, Loss: 0.05170283801853657
Epoch 7/10, Loss: 0.02387611297890544
Epoch 8/10, Loss: 0.1631831184029579
Epoch 9/10, Loss: 0.08204342573881149
Epoch 10/10, Loss: 0.07013023868203164
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 0.70 0.74 0.72 19
Pipes 1.00 0.76 0.87 17
Rockets 0.47 0.64 0.54 11
Vehicles 0.58 0.58 0.58 12
accuracy 0.71 70
macro avg 0.73 0.71 0.71 70
weighted avg 0.75 0.71 0.72 70
Training resnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.5034607887268066
Epoch 2/10, Loss: 0.9488541007041931
Epoch 3/10, Loss: 0.44834885597229
Epoch 4/10, Loss: 0.16418254375457764
Epoch 5/10, Loss: 0.04710474610328674
Epoch 6/10, Loss: 0.0641149953007698
Epoch 7/10, Loss: 0.07732243426144123
Epoch 8/10, Loss: 0.05982809960842132
Epoch 9/10, Loss: 0.07223520539700985
Epoch 10/10, Loss: 0.03766770623624325
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.92 0.63 0.75 19
Pipes 1.00 0.76 0.87 17
Rockets 0.61 1.00 0.76 11
Vehicles 0.64 0.75 0.69 12
accuracy 0.80 70
macro avg 0.82 0.83 0.80 70
weighted avg 0.84 0.80 0.80 70
Training resnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.4480091333389282
Epoch 2/10, Loss: 0.7156906664371491
Epoch 3/10, Loss: 0.20406692177057267
Epoch 4/10, Loss: 0.05092548318207264
Epoch 5/10, Loss: 0.052653341367840764
Epoch 6/10, Loss: 0.059808761440217496
Epoch 7/10, Loss: 0.02465339284390211
Epoch 8/10, Loss: 0.043697479739785196
Epoch 9/10, Loss: 0.1398419264703989
Epoch 10/10, Loss: 0.1998220667243004
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 0.77 0.89 0.83 19
Pipes 0.74 0.82 0.78 17
Rockets 0.73 0.73 0.73 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.80 70
macro avg 0.83 0.79 0.80 70
weighted avg 0.82 0.80 0.80 70
Training resnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.481545639038086
Epoch 2/10, Loss: 0.8639334678649903
Epoch 3/10, Loss: 0.2974187761545181
Epoch 4/10, Loss: 0.0877578467130661
Epoch 5/10, Loss: 0.08400304429233074
Epoch 6/10, Loss: 0.05541416704654693
Epoch 7/10, Loss: 0.07481687646359206
Epoch 8/10, Loss: 0.07087626159191132
Epoch 9/10, Loss: 0.02534730061888695
Epoch 10/10, Loss: 0.09010839331895112
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 1.00 0.89 0.94 19
Pipes 1.00 0.88 0.94 17
Rockets 0.69 0.82 0.75 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.89 70
macro avg 0.89 0.89 0.88 70
weighted avg 0.91 0.89 0.89 70
Training resnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5384161472320557
Epoch 2/10, Loss: 1.036775004863739
Epoch 3/10, Loss: 0.5157387971878051
Epoch 4/10, Loss: 0.2040394961833954
Epoch 5/10, Loss: 0.10377594754099846
Epoch 6/10, Loss: 0.07310117110610008
Epoch 7/10, Loss: 0.13198775984346867
Epoch 8/10, Loss: 0.06448204778134822
Epoch 9/10, Loss: 0.055773529410362246
Epoch 10/10, Loss: 0.027900719828903676
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.82 0.78 11
Mines 0.87 0.68 0.76 19
Pipes 0.93 0.82 0.88 17
Rockets 0.62 0.73 0.67 11
Vehicles 0.67 0.83 0.74 12
accuracy 0.77 70
macro avg 0.77 0.78 0.77 70
weighted avg 0.79 0.77 0.77 70
Training resnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.448414134979248
Epoch 2/10, Loss: 0.7377818465232849
Epoch 3/10, Loss: 0.2403217911720276
Epoch 4/10, Loss: 0.10657520256936551
Epoch 5/10, Loss: 0.12362651601433754
Epoch 6/10, Loss: 0.053946223109960556
Epoch 7/10, Loss: 0.05990412458777428
Epoch 8/10, Loss: 0.11992813423275947
Epoch 9/10, Loss: 0.04804485850036144
Epoch 10/10, Loss: 0.07405033186078072
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.91 0.69 11
Mines 0.83 0.79 0.81 19
Pipes 1.00 0.71 0.83 17
Rockets 0.83 0.91 0.87 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.81 70
macro avg 0.84 0.83 0.82 70
weighted avg 0.86 0.81 0.82 70
Training resnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.4865453958511352
Epoch 2/10, Loss: 0.8432370066642761
Epoch 3/10, Loss: 0.3009850442409515
Epoch 4/10, Loss: 0.11477303504943848
Epoch 5/10, Loss: 0.13115293346345425
Epoch 6/10, Loss: 0.13121905364096165
Epoch 7/10, Loss: 0.1064015232026577
Epoch 8/10, Loss: 0.1664888322353363
Epoch 9/10, Loss: 0.07804114446043968
Epoch 10/10, Loss: 0.07921870462596417
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.46 1.00 0.63 11
Mines 0.75 0.47 0.58 19
Pipes 1.00 0.71 0.83 17
Rockets 0.69 1.00 0.81 11
Vehicles 1.00 0.50 0.67 12
accuracy 0.70 70
macro avg 0.78 0.74 0.70 70
weighted avg 0.80 0.70 0.70 70
Training resnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.500606632232666
Epoch 2/10, Loss: 0.9552056312561035
Epoch 3/10, Loss: 0.4277254581451416
Epoch 4/10, Loss: 0.18038518279790877
Epoch 5/10, Loss: 0.06743664741516113
Epoch 6/10, Loss: 0.05181586518883705
Epoch 7/10, Loss: 0.06544867344200611
Epoch 8/10, Loss: 0.0796316348016262
Epoch 9/10, Loss: 0.06862198654562235
Epoch 10/10, Loss: 0.0645612832158804
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.41 0.82 0.55 11
Mines 0.82 0.74 0.78 19
Pipes 1.00 0.71 0.83 17
Rockets 0.67 0.55 0.60 11
Vehicles 0.80 0.67 0.73 12
accuracy 0.70 70
macro avg 0.74 0.69 0.70 70
weighted avg 0.77 0.70 0.72 70
Training resnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6152043342590332
Epoch 2/10, Loss: 1.5927974700927734
Epoch 3/10, Loss: 1.56989483833313
Epoch 4/10, Loss: 1.545750117301941
Epoch 5/10, Loss: 1.5245241403579712
Epoch 6/10, Loss: 1.503148341178894
Epoch 7/10, Loss: 1.474990463256836
Epoch 8/10, Loss: 1.4290625333786011
Epoch 9/10, Loss: 1.4242701292037965
Epoch 10/10, Loss: 1.4056331157684325
Accuracy: 34.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.31 0.36 0.33 11
Mines 0.50 0.11 0.17 19
Pipes 1.00 0.12 0.21 17
Rockets 0.36 0.45 0.40 11
Vehicles 0.30 0.92 0.45 12
accuracy 0.34 70
macro avg 0.49 0.39 0.31 70
weighted avg 0.53 0.34 0.29 70
Training resnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6094778537750245
Epoch 2/10, Loss: 1.5894943952560425
Epoch 3/10, Loss: 1.5899095058441162
Epoch 4/10, Loss: 1.5621328830718995
Epoch 5/10, Loss: 1.5453407049179078
Epoch 6/10, Loss: 1.5183078050613403
Epoch 7/10, Loss: 1.4879905700683593
Epoch 8/10, Loss: 1.4690950870513917
Epoch 9/10, Loss: 1.448126983642578
Epoch 10/10, Loss: 1.431783413887024
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.64 0.56 11
Mines 1.00 0.11 0.19 19
Pipes 1.00 0.18 0.30 17
Rockets 0.60 0.27 0.38 11
Vehicles 0.24 0.92 0.38 12
accuracy 0.37 70
macro avg 0.67 0.42 0.36 70
weighted avg 0.73 0.37 0.34 70
Training resnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6186256885528565
Epoch 2/10, Loss: 1.6173427104949951
Epoch 3/10, Loss: 1.5869601488113403
Epoch 4/10, Loss: 1.5819676160812377
Epoch 5/10, Loss: 1.565846562385559
Epoch 6/10, Loss: 1.5225844621658324
Epoch 7/10, Loss: 1.5086917638778687
Epoch 8/10, Loss: 1.4835430145263673
Epoch 9/10, Loss: 1.466741681098938
Epoch 10/10, Loss: 1.463921070098877
Accuracy: 31.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.45 0.59 11
Mines 0.50 0.21 0.30 19
Pipes 1.00 0.00 0.00 17
Rockets 0.29 0.18 0.22 11
Vehicles 0.22 0.92 0.36 12
accuracy 0.31 70
macro avg 0.57 0.35 0.29 70
weighted avg 0.59 0.31 0.27 70
Training resnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6367398738861083
Epoch 2/10, Loss: 1.6226738929748534
Epoch 3/10, Loss: 1.5952097177505493
Epoch 4/10, Loss: 1.5951090574264526
Epoch 5/10, Loss: 1.562512707710266
Epoch 6/10, Loss: 1.5442883491516113
Epoch 7/10, Loss: 1.519063663482666
Epoch 8/10, Loss: 1.500194501876831
Epoch 9/10, Loss: 1.4760985612869262
Epoch 10/10, Loss: 1.4389618158340454
Accuracy: 25.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.30 0.73 0.42 11
Mines 0.00 0.00 0.00 19
Pipes 1.00 0.12 0.21 17
Rockets 0.29 0.18 0.22 11
Vehicles 0.20 0.50 0.29 12
accuracy 0.26 70
macro avg 0.36 0.31 0.23 70
weighted avg 0.37 0.26 0.20 70
Training resnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.611820125579834
Epoch 2/10, Loss: 1.603498673439026
Epoch 3/10, Loss: 1.5747786283493042
Epoch 4/10, Loss: 1.5829864978790282
Epoch 5/10, Loss: 1.5602909564971923
Epoch 6/10, Loss: 1.5460283756256104
Epoch 7/10, Loss: 1.5365890264511108
Epoch 8/10, Loss: 1.5210289478302002
Epoch 9/10, Loss: 1.4835518836975097
Epoch 10/10, Loss: 1.4690688133239747
Accuracy: 28.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.45 0.45 0.45 11
Mines 0.50 0.21 0.30 19
Pipes 1.00 0.00 0.00 17
Rockets 0.50 0.09 0.15 11
Vehicles 0.20 0.83 0.33 12
accuracy 0.29 70
macro avg 0.53 0.32 0.25 70
weighted avg 0.56 0.29 0.23 70
Training resnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5999361276626587
Epoch 2/10, Loss: 1.569346809387207
Epoch 3/10, Loss: 1.57063570022583
Epoch 4/10, Loss: 1.5464123249053956
Epoch 5/10, Loss: 1.5347684860229491
Epoch 6/10, Loss: 1.5281703472137451
Epoch 7/10, Loss: 1.4920144081115723
Epoch 8/10, Loss: 1.4678133487701417
Epoch 9/10, Loss: 1.4548181772232056
Epoch 10/10, Loss: 1.430459213256836
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.36 0.42 11
Mines 0.50 0.05 0.10 19
Pipes 1.00 0.00 0.00 17
Rockets 0.22 0.18 0.20 11
Vehicles 0.24 1.00 0.38 12
accuracy 0.27 70
macro avg 0.49 0.32 0.22 70
weighted avg 0.53 0.27 0.19 70
Training resnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6093420743942262
Epoch 2/10, Loss: 1.6053913116455079
Epoch 3/10, Loss: 1.5878774166107177
Epoch 4/10, Loss: 1.5762047052383423
Epoch 5/10, Loss: 1.553069806098938
Epoch 6/10, Loss: 1.5313111782073974
Epoch 7/10, Loss: 1.514244794845581
Epoch 8/10, Loss: 1.4818865537643433
Epoch 9/10, Loss: 1.4552494525909423
Epoch 10/10, Loss: 1.421034836769104
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.45 0.50 11
Mines 0.56 0.26 0.36 19
Pipes 0.80 0.24 0.36 17
Rockets 0.14 0.09 0.11 11
Vehicles 0.28 0.92 0.42 12
accuracy 0.37 70
macro avg 0.47 0.39 0.35 70
weighted avg 0.50 0.37 0.35 70
Training resnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6354912519454956
Epoch 2/10, Loss: 1.6225030660629272
Epoch 3/10, Loss: 1.6047220945358276
Epoch 4/10, Loss: 1.5964974403381347
Epoch 5/10, Loss: 1.5792558431625365
Epoch 6/10, Loss: 1.5576501369476319
Epoch 7/10, Loss: 1.5355252504348755
Epoch 8/10, Loss: 1.5147606134414673
Epoch 9/10, Loss: 1.4891171216964723
Epoch 10/10, Loss: 1.485822582244873
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.40 0.36 0.38 11
Mines 0.33 0.05 0.09 19
Pipes 1.00 0.00 0.00 17
Rockets 0.17 0.18 0.17 11
Vehicles 0.22 0.83 0.35 12
accuracy 0.24 70
macro avg 0.42 0.29 0.20 70
weighted avg 0.46 0.24 0.17 70
Training resnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5984198808670045
Epoch 2/10, Loss: 1.5767683744430543
Epoch 3/10, Loss: 1.5751120090484618
Epoch 4/10, Loss: 1.5615155458450318
Epoch 5/10, Loss: 1.5359046459197998
Epoch 6/10, Loss: 1.513864016532898
Epoch 7/10, Loss: 1.51592116355896
Epoch 8/10, Loss: 1.4982552766799926
Epoch 9/10, Loss: 1.4591882228851318
Epoch 10/10, Loss: 1.4348425149917603
Accuracy: 38.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.39 0.64 0.48 11
Mines 0.71 0.26 0.38 19
Pipes 1.00 0.29 0.45 17
Rockets 0.40 0.18 0.25 11
Vehicles 0.23 0.67 0.34 12
accuracy 0.39 70
macro avg 0.55 0.41 0.38 70
weighted avg 0.60 0.39 0.39 70
Training resnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.7871700048446655
Epoch 2/10, Loss: 1.5765423774719238
Epoch 3/10, Loss: 1.3467211961746215
Epoch 4/10, Loss: 1.2532025098800659
Epoch 5/10, Loss: 1.0239376902580262
Epoch 6/10, Loss: 0.8224114418029785
Epoch 7/10, Loss: 0.6179126858711242
Epoch 8/10, Loss: 0.5332558572292327
Epoch 9/10, Loss: 0.46534970998764036
Epoch 10/10, Loss: 0.4088677793741226
Accuracy: 62.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.47 0.73 0.57 11
Mines 0.91 0.53 0.67 19
Pipes 1.00 0.71 0.83 17
Rockets 0.41 1.00 0.58 11
Vehicles 1.00 0.25 0.40 12
accuracy 0.63 70
macro avg 0.76 0.64 0.61 70
weighted avg 0.80 0.63 0.63 70
Training resnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.8485763311386108
Epoch 2/10, Loss: 1.6582717657089234
Epoch 3/10, Loss: 1.4460055112838746
Epoch 4/10, Loss: 1.2487682580947876
Epoch 5/10, Loss: 1.124095606803894
Epoch 6/10, Loss: 1.019221031665802
Epoch 7/10, Loss: 0.9334327578544617
Epoch 8/10, Loss: 0.8420052647590637
Epoch 9/10, Loss: 0.8284677863121033
Epoch 10/10, Loss: 0.8443415641784668
Accuracy: 47.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.46 0.55 0.50 11
Mines 0.36 0.26 0.30 19
Pipes 0.80 0.47 0.59 17
Rockets 0.50 0.55 0.52 11
Vehicles 0.38 0.67 0.48 12
accuracy 0.47 70
macro avg 0.50 0.50 0.48 70
weighted avg 0.51 0.47 0.47 70
Training resnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.4816978216171264
Epoch 2/10, Loss: 1.8479326248168946
Epoch 3/10, Loss: 1.344406008720398
Epoch 4/10, Loss: 1.1304613947868347
Epoch 5/10, Loss: 0.9686880111694336
Epoch 6/10, Loss: 1.0633779525756837
Epoch 7/10, Loss: 0.9407654047012329
Epoch 8/10, Loss: 0.7819475412368775
Epoch 9/10, Loss: 0.6232419729232788
Epoch 10/10, Loss: 0.6027578115463257
Accuracy: 67.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.55 0.55 0.55 11
Mines 0.63 0.63 0.63 19
Pipes 0.65 0.65 0.65 17
Rockets 0.69 0.82 0.75 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.67 70
macro avg 0.68 0.68 0.68 70
weighted avg 0.68 0.67 0.67 70
Training resnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.4639782190322876
Epoch 2/10, Loss: 2.1859172582626343
Epoch 3/10, Loss: 1.536750078201294
Epoch 4/10, Loss: 1.44237060546875
Epoch 5/10, Loss: 1.3608649969100952
Epoch 6/10, Loss: 1.423519206047058
Epoch 7/10, Loss: 1.4228199243545532
Epoch 8/10, Loss: 1.3274231672286987
Epoch 9/10, Loss: 1.2817327976226807
Epoch 10/10, Loss: 1.2519760370254516
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.57 0.21 0.31 19
Pipes 1.00 0.00 0.00 17
Rockets 0.15 0.55 0.24 11
Vehicles 0.30 0.58 0.40 12
accuracy 0.24 70
macro avg 0.61 0.27 0.19 70
weighted avg 0.63 0.24 0.19 70
Training resnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6328411102294922
Epoch 2/10, Loss: 1.7363322496414184
Epoch 3/10, Loss: 1.3345756769180297
Epoch 4/10, Loss: 1.5231006383895873
Epoch 5/10, Loss: 1.4473935842514039
Epoch 6/10, Loss: 1.220743680000305
Epoch 7/10, Loss: 1.018516969680786
Epoch 8/10, Loss: 0.9549245238304138
Epoch 9/10, Loss: 0.7804414510726929
Epoch 10/10, Loss: 0.7899908900260926
Accuracy: 44.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.55 0.60 11
Mines 0.25 0.11 0.15 19
Pipes 0.57 0.47 0.52 17
Rockets 0.37 0.64 0.47 11
Vehicles 0.40 0.67 0.50 12
accuracy 0.44 70
macro avg 0.45 0.48 0.45 70
weighted avg 0.44 0.44 0.42 70
Training resnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5696247577667237
Epoch 2/10, Loss: 1.535622000694275
Epoch 3/10, Loss: 1.28952054977417
Epoch 4/10, Loss: 1.1929540991783143
Epoch 5/10, Loss: 1.0518141269683838
Epoch 6/10, Loss: 1.0236381411552429
Epoch 7/10, Loss: 0.7552725911140442
Epoch 8/10, Loss: 0.8092203855514526
Epoch 9/10, Loss: 0.8752571105957031
Epoch 10/10, Loss: 0.5998706161975861
Accuracy: 61.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.64 0.64 11
Mines 1.00 0.32 0.48 19
Pipes 0.72 0.76 0.74 17
Rockets 0.42 0.91 0.57 11
Vehicles 0.64 0.58 0.61 12
accuracy 0.61 70
macro avg 0.68 0.64 0.61 70
weighted avg 0.72 0.61 0.60 70
Training resnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6104000329971313
Epoch 2/10, Loss: 1.6447544813156127
Epoch 3/10, Loss: 1.4584911823272706
Epoch 4/10, Loss: 1.2687633752822876
Epoch 5/10, Loss: 1.2224861621856689
Epoch 6/10, Loss: 1.1594056844711305
Epoch 7/10, Loss: 1.0961341857910156
Epoch 8/10, Loss: 1.1149592399597168
Epoch 9/10, Loss: 1.0051329016685486
Epoch 10/10, Loss: 1.0076663970947266
Accuracy: 41.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.47 0.74 0.57 19
Pipes 1.00 0.06 0.11 17
Rockets 0.31 0.36 0.33 11
Vehicles 0.38 0.83 0.53 12
accuracy 0.41 70
macro avg 0.63 0.40 0.31 70
weighted avg 0.64 0.41 0.32 70
Training resnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7855636596679687
Epoch 2/10, Loss: 1.8474735260009765
Epoch 3/10, Loss: 1.5385473489761352
Epoch 4/10, Loss: 1.4481587171554566
Epoch 5/10, Loss: 1.3870301008224488
Epoch 6/10, Loss: 1.271628165245056
Epoch 7/10, Loss: 1.18973548412323
Epoch 8/10, Loss: 1.0685767769813537
Epoch 9/10, Loss: 0.9521005630493165
Epoch 10/10, Loss: 0.8526371121406555
Accuracy: 62.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.55 0.63 11
Mines 0.77 0.53 0.62 19
Pipes 0.59 0.94 0.73 17
Rockets 0.43 0.27 0.33 11
Vehicles 0.60 0.75 0.67 12
accuracy 0.63 70
macro avg 0.63 0.61 0.60 70
weighted avg 0.64 0.63 0.61 70
Training resnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.844524073600769
Epoch 2/10, Loss: 1.530378794670105
Epoch 3/10, Loss: 1.380287265777588
Epoch 4/10, Loss: 1.2524028778076173
Epoch 5/10, Loss: 1.0229623317718506
Epoch 6/10, Loss: 0.9677602648735046
Epoch 7/10, Loss: 0.8101842284202576
Epoch 8/10, Loss: 0.5923465192317963
Epoch 9/10, Loss: 0.5891679048538208
Epoch 10/10, Loss: 0.45333564281463623
Accuracy: 64.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.45 0.59 11
Mines 0.59 0.68 0.63 19
Pipes 0.64 0.82 0.72 17
Rockets 0.50 0.36 0.42 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.64 70
macro avg 0.66 0.62 0.62 70
weighted avg 0.65 0.64 0.63 70
Training resnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.5530238350232441
Epoch 2/10, Loss: 1.1866362161106534
Epoch 3/10, Loss: 0.7083404776122835
Epoch 4/10, Loss: 0.31750379419989055
Epoch 5/10, Loss: 0.16888181451294157
Epoch 6/10, Loss: 0.06909754354920652
Epoch 7/10, Loss: 0.10057383206569487
Epoch 8/10, Loss: 0.05451695564099484
Epoch 9/10, Loss: 0.0544228863178028
Epoch 10/10, Loss: 0.0710470027083324
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.90 0.95 0.92 19
Pipes 0.94 0.88 0.91 17
Rockets 0.82 0.82 0.82 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.86 70
macro avg 0.86 0.84 0.84 70
weighted avg 0.88 0.86 0.86 70
Training resnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.5438986553086176
Epoch 2/10, Loss: 1.2449122932222154
Epoch 3/10, Loss: 0.8202828466892242
Epoch 4/10, Loss: 0.4314807703097661
Epoch 5/10, Loss: 0.16280491815672982
Epoch 6/10, Loss: 0.08631353494193819
Epoch 7/10, Loss: 0.07496862123823828
Epoch 8/10, Loss: 0.03284898917708132
Epoch 9/10, Loss: 0.04142489920680722
Epoch 10/10, Loss: 0.05510578043241468
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.86 0.95 0.90 19
Pipes 0.88 0.88 0.88 17
Rockets 0.64 0.82 0.72 11
Vehicles 1.00 0.50 0.67 12
accuracy 0.83 70
macro avg 0.84 0.81 0.81 70
weighted avg 0.85 0.83 0.82 70
Training resnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.558614730834961
Epoch 2/10, Loss: 1.3216507169935439
Epoch 3/10, Loss: 1.0082352591885462
Epoch 4/10, Loss: 0.6343595683574677
Epoch 5/10, Loss: 0.316306004093753
Epoch 6/10, Loss: 0.18885377442671192
Epoch 7/10, Loss: 0.0943079919864734
Epoch 8/10, Loss: 0.06971705787711674
Epoch 9/10, Loss: 0.04821914491347141
Epoch 10/10, Loss: 0.02730721892375085
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 0.95 1.00 0.97 19
Pipes 1.00 0.94 0.97 17
Rockets 0.79 1.00 0.88 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.91 70
macro avg 0.91 0.90 0.90 70
weighted avg 0.92 0.91 0.91 70
Training resnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.544753180609809
Epoch 2/10, Loss: 1.1864514814482794
Epoch 3/10, Loss: 0.7188453872998556
Epoch 4/10, Loss: 0.36023089786370593
Epoch 5/10, Loss: 0.19865109854274327
Epoch 6/10, Loss: 0.12515053815311855
Epoch 7/10, Loss: 0.07680521698461638
Epoch 8/10, Loss: 0.045225559940768614
Epoch 9/10, Loss: 0.08932994880403082
Epoch 10/10, Loss: 0.04755962189907829
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.81 0.89 0.85 19
Pipes 0.89 0.94 0.91 17
Rockets 0.83 0.91 0.87 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.86 70
macro avg 0.87 0.85 0.85 70
weighted avg 0.87 0.86 0.85 70
Training resnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5495008163981967
Epoch 2/10, Loss: 1.2497137387593586
Epoch 3/10, Loss: 0.8552836875120798
Epoch 4/10, Loss: 0.4436776373121474
Epoch 5/10, Loss: 0.20341596007347107
Epoch 6/10, Loss: 0.08262329776253965
Epoch 7/10, Loss: 0.05938307527038786
Epoch 8/10, Loss: 0.05737740541290906
Epoch 9/10, Loss: 0.05931401816714141
Epoch 10/10, Loss: 0.0767370392051008
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.79 0.79 0.79 19
Pipes 0.85 1.00 0.92 17
Rockets 0.89 0.73 0.80 11
Vehicles 0.86 0.50 0.63 12
accuracy 0.81 70
macro avg 0.82 0.80 0.80 70
weighted avg 0.82 0.81 0.80 70
Training resnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.564439230495029
Epoch 2/10, Loss: 1.3473614321814642
Epoch 3/10, Loss: 0.9594853222370148
Epoch 4/10, Loss: 0.6052782651450899
Epoch 5/10, Loss: 0.28950243360466427
Epoch 6/10, Loss: 0.17874961387779978
Epoch 7/10, Loss: 0.10143554396927357
Epoch 8/10, Loss: 0.08141725230962038
Epoch 9/10, Loss: 0.05557065958985024
Epoch 10/10, Loss: 0.060886713676154613
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.61 1.00 0.76 11
Mines 0.76 0.84 0.80 19
Pipes 1.00 0.94 0.97 17
Rockets 0.88 0.64 0.74 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.81 70
macro avg 0.85 0.80 0.80 70
weighted avg 0.85 0.81 0.81 70
Training resnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5162883798281352
Epoch 2/10, Loss: 1.100064433283276
Epoch 3/10, Loss: 0.6260233372449875
Epoch 4/10, Loss: 0.2887050931652387
Epoch 5/10, Loss: 0.1492085283001264
Epoch 6/10, Loss: 0.08093695528805256
Epoch 7/10, Loss: 0.08513620940761434
Epoch 8/10, Loss: 0.047014149319794446
Epoch 9/10, Loss: 0.035628624053464994
Epoch 10/10, Loss: 0.05644008434481091
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.88 0.79 0.83 19
Pipes 0.64 0.94 0.76 17
Rockets 0.82 0.82 0.82 11
Vehicles 1.00 0.33 0.50 12
accuracy 0.77 70
macro avg 0.82 0.76 0.75 70
weighted avg 0.82 0.77 0.76 70
Training resnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.563224772612254
Epoch 2/10, Loss: 1.2497605019145541
Epoch 3/10, Loss: 0.8734969099362692
Epoch 4/10, Loss: 0.4877024706867006
Epoch 5/10, Loss: 0.25330907023615307
Epoch 6/10, Loss: 0.12811735976073477
Epoch 7/10, Loss: 0.1224852389552527
Epoch 8/10, Loss: 0.08009273641639286
Epoch 9/10, Loss: 0.04583137539318866
Epoch 10/10, Loss: 0.03184151271772054
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.88 0.64 0.74 11
Mines 0.75 0.95 0.84 19
Pipes 1.00 0.94 0.97 17
Rockets 0.82 0.82 0.82 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.86 70
macro avg 0.87 0.84 0.85 70
weighted avg 0.87 0.86 0.86 70
Training resnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5579333570268419
Epoch 2/10, Loss: 1.3086575004789565
Epoch 3/10, Loss: 1.0057961874537997
Epoch 4/10, Loss: 0.585244486729304
Epoch 5/10, Loss: 0.32815758056110805
Epoch 6/10, Loss: 0.19134784241517386
Epoch 7/10, Loss: 0.11090005685885747
Epoch 8/10, Loss: 0.09712599973297781
Epoch 9/10, Loss: 0.10026049727780952
Epoch 10/10, Loss: 0.07730160601851013
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.86 0.95 0.90 19
Pipes 0.89 0.94 0.91 17
Rockets 0.77 0.91 0.83 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.87 70
macro avg 0.88 0.86 0.86 70
weighted avg 0.88 0.87 0.87 70
Training resnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6196338931719463
Epoch 2/10, Loss: 1.5963774919509888
Epoch 3/10, Loss: 1.59803393152025
Epoch 4/10, Loss: 1.5755050314797296
Epoch 5/10, Loss: 1.564664708243476
Epoch 6/10, Loss: 1.5479843881395128
Epoch 7/10, Loss: 1.5326878163549635
Epoch 8/10, Loss: 1.5320490135086908
Epoch 9/10, Loss: 1.5151459177335103
Epoch 10/10, Loss: 1.4933488368988037
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.33 0.18 0.24 11
Mines 0.50 0.16 0.24 19
Pipes 1.00 0.00 0.00 17
Rockets 0.20 0.09 0.12 11
Vehicles 0.19 0.83 0.31 12
accuracy 0.23 70
macro avg 0.44 0.25 0.18 70
weighted avg 0.49 0.23 0.17 70
Training resnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6153814329041376
Epoch 2/10, Loss: 1.5924744804700215
Epoch 3/10, Loss: 1.5895791318681505
Epoch 4/10, Loss: 1.5616239706675212
Epoch 5/10, Loss: 1.5498114360703363
Epoch 6/10, Loss: 1.5432223147816129
Epoch 7/10, Loss: 1.5302916301621332
Epoch 8/10, Loss: 1.514762196275923
Epoch 9/10, Loss: 1.5024553140004475
Epoch 10/10, Loss: 1.5049796832932367
Accuracy: 28.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.22 0.18 0.20 11
Mines 0.60 0.16 0.25 19
Pipes 1.00 0.12 0.21 17
Rockets 0.75 0.27 0.40 11
Vehicles 0.20 0.83 0.32 12
accuracy 0.29 70
macro avg 0.55 0.31 0.28 70
weighted avg 0.59 0.29 0.27 70
Training resnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.622295657793681
Epoch 2/10, Loss: 1.595631288157569
Epoch 3/10, Loss: 1.5868010256025527
Epoch 4/10, Loss: 1.5769258870018854
Epoch 5/10, Loss: 1.5577276349067688
Epoch 6/10, Loss: 1.5605162448353238
Epoch 7/10, Loss: 1.5463288492626615
Epoch 8/10, Loss: 1.5365961657630072
Epoch 9/10, Loss: 1.5093612141079373
Epoch 10/10, Loss: 1.5004241665204365
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.22 0.18 0.20 11
Mines 0.25 0.05 0.09 19
Pipes 1.00 0.00 0.00 17
Rockets 0.20 0.18 0.19 11
Vehicles 0.23 0.92 0.37 12
accuracy 0.23 70
macro avg 0.38 0.27 0.17 70
weighted avg 0.42 0.23 0.15 70
Training resnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.61546081966824
Epoch 2/10, Loss: 1.607994708749983
Epoch 3/10, Loss: 1.5924843218591478
Epoch 4/10, Loss: 1.5793113973405626
Epoch 5/10, Loss: 1.581411878267924
Epoch 6/10, Loss: 1.566237250963847
Epoch 7/10, Loss: 1.5573941601647272
Epoch 8/10, Loss: 1.5488309131728277
Epoch 9/10, Loss: 1.5396372410986159
Epoch 10/10, Loss: 1.5124741660224066
Accuracy: 30.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.31 0.36 0.33 11
Mines 0.67 0.21 0.32 19
Pipes 1.00 0.00 0.00 17
Rockets 0.75 0.27 0.40 11
Vehicles 0.21 0.83 0.34 12
accuracy 0.30 70
macro avg 0.59 0.34 0.28 70
weighted avg 0.63 0.30 0.26 70
Training resnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6072501805093553
Epoch 2/10, Loss: 1.604721360736423
Epoch 3/10, Loss: 1.5774605870246887
Epoch 4/10, Loss: 1.5625099672211542
Epoch 5/10, Loss: 1.5545252230432298
Epoch 6/10, Loss: 1.5448200503985088
Epoch 7/10, Loss: 1.5292733046743605
Epoch 8/10, Loss: 1.513090067439609
Epoch 9/10, Loss: 1.528354869948493
Epoch 10/10, Loss: 1.4890298909611173
Accuracy: 48.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.53 0.73 0.62 11
Mines 0.50 0.37 0.42 19
Pipes 1.00 0.29 0.45 17
Rockets 1.00 0.27 0.43 11
Vehicles 0.33 0.92 0.49 12
accuracy 0.49 70
macro avg 0.67 0.52 0.48 70
weighted avg 0.68 0.49 0.47 70
Training resnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6050107147958543
Epoch 2/10, Loss: 1.5963401595751445
Epoch 3/10, Loss: 1.5908393859863281
Epoch 4/10, Loss: 1.5775361127323575
Epoch 5/10, Loss: 1.5843333270814683
Epoch 6/10, Loss: 1.5533891783820257
Epoch 7/10, Loss: 1.536509308550093
Epoch 8/10, Loss: 1.5268515282207065
Epoch 9/10, Loss: 1.5381809274355571
Epoch 10/10, Loss: 1.508950670560201
Accuracy: 31.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.38 0.45 0.42 11
Mines 0.44 0.37 0.40 19
Pipes 1.00 0.00 0.00 17
Rockets 0.38 0.27 0.32 11
Vehicles 0.21 0.58 0.31 12
accuracy 0.31 70
macro avg 0.48 0.34 0.29 70
weighted avg 0.52 0.31 0.28 70
Training resnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.613612214724223
Epoch 2/10, Loss: 1.5996424357096355
Epoch 3/10, Loss: 1.5915491514735751
Epoch 4/10, Loss: 1.570300731394026
Epoch 5/10, Loss: 1.5547564691967435
Epoch 6/10, Loss: 1.5517116785049438
Epoch 7/10, Loss: 1.5311647057533264
Epoch 8/10, Loss: 1.512621541817983
Epoch 9/10, Loss: 1.504311932457818
Epoch 10/10, Loss: 1.4920844501919217
Accuracy: 31.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.45 0.45 0.45 11
Mines 0.17 0.05 0.08 19
Pipes 0.83 0.29 0.43 17
Rockets 0.33 0.09 0.14 11
Vehicles 0.23 0.83 0.36 12
accuracy 0.31 70
macro avg 0.40 0.35 0.29 70
weighted avg 0.41 0.31 0.28 70
Training resnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6209848986731634
Epoch 2/10, Loss: 1.603919439845615
Epoch 3/10, Loss: 1.5858657426304288
Epoch 4/10, Loss: 1.567775547504425
Epoch 5/10, Loss: 1.5694084366162617
Epoch 6/10, Loss: 1.548630072010888
Epoch 7/10, Loss: 1.5372115638520982
Epoch 8/10, Loss: 1.5256927476988897
Epoch 9/10, Loss: 1.5192169613308377
Epoch 10/10, Loss: 1.5041144556469388
Accuracy: 31.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.33 0.18 0.24 11
Mines 0.80 0.21 0.33 19
Pipes 1.00 0.12 0.21 17
Rockets 0.38 0.27 0.32 11
Vehicles 0.22 0.92 0.36 12
accuracy 0.31 70
macro avg 0.55 0.34 0.29 70
weighted avg 0.61 0.31 0.29 70
Training resnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6150979730818007
Epoch 2/10, Loss: 1.598105874326494
Epoch 3/10, Loss: 1.5964665810267131
Epoch 4/10, Loss: 1.603857742415534
Epoch 5/10, Loss: 1.5806332892841763
Epoch 6/10, Loss: 1.5717275473806593
Epoch 7/10, Loss: 1.5559723443455167
Epoch 8/10, Loss: 1.5373681386311848
Epoch 9/10, Loss: 1.5372894671228197
Epoch 10/10, Loss: 1.526575247446696
Accuracy: 32.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.33 0.45 0.38 11
Mines 0.80 0.21 0.33 19
Pipes 1.00 0.00 0.00 17
Rockets 0.40 0.18 0.25 11
Vehicles 0.27 1.00 0.42 12
accuracy 0.33 70
macro avg 0.56 0.37 0.28 70
weighted avg 0.62 0.33 0.26 70
Training resnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.2681321899096172
Epoch 2/10, Loss: 0.4506610482931137
Epoch 3/10, Loss: 0.20802397073970902
Epoch 4/10, Loss: 0.13083010104795298
Epoch 5/10, Loss: 0.07111673305432002
Epoch 6/10, Loss: 0.11664545675739646
Epoch 7/10, Loss: 0.04232644481170508
Epoch 8/10, Loss: 0.026903853079097137
Epoch 9/10, Loss: 0.016688709108469386
Epoch 10/10, Loss: 0.012612700837457346
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.62 0.91 0.74 11
Mines 0.83 0.79 0.81 19
Pipes 1.00 0.94 0.97 17
Rockets 0.75 0.82 0.78 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.83 70
macro avg 0.84 0.82 0.82 70
weighted avg 0.86 0.83 0.83 70
Training resnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.2954222460587819
Epoch 2/10, Loss: 0.5320264597733816
Epoch 3/10, Loss: 0.2080324006577333
Epoch 4/10, Loss: 0.07969754468649626
Epoch 5/10, Loss: 0.05131250728542606
Epoch 6/10, Loss: 0.07601133151911199
Epoch 7/10, Loss: 0.0449428195392506
Epoch 8/10, Loss: 0.02375926852174517
Epoch 9/10, Loss: 0.049856955366623074
Epoch 10/10, Loss: 0.02961910097575229
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.89 0.89 0.89 19
Pipes 1.00 0.88 0.94 17
Rockets 0.62 0.73 0.67 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.87 70
macro avg 0.87 0.87 0.86 70
weighted avg 0.89 0.87 0.88 70
Training resnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.2977683742841084
Epoch 2/10, Loss: 0.4929079082277086
Epoch 3/10, Loss: 0.22665563805235756
Epoch 4/10, Loss: 0.1132707219156954
Epoch 5/10, Loss: 0.08686621663057142
Epoch 6/10, Loss: 0.03477099294670754
Epoch 7/10, Loss: 0.03848655397693316
Epoch 8/10, Loss: 0.12324733659625053
Epoch 9/10, Loss: 0.04244819164483084
Epoch 10/10, Loss: 0.06781508287207948
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 1.00 1.00 11
Mines 0.74 0.89 0.81 19
Pipes 0.84 0.94 0.89 17
Rockets 0.83 0.91 0.87 11
Vehicles 1.00 0.42 0.59 12
accuracy 0.84 70
macro avg 0.88 0.83 0.83 70
weighted avg 0.86 0.84 0.83 70
Training resnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.14345093899303
Epoch 2/10, Loss: 0.4493042263719771
Epoch 3/10, Loss: 0.25187576355205643
Epoch 4/10, Loss: 0.11108991679632002
Epoch 5/10, Loss: 0.12471268662355012
Epoch 6/10, Loss: 0.06938814997879995
Epoch 7/10, Loss: 0.06063099448672599
Epoch 8/10, Loss: 0.03613742872969144
Epoch 9/10, Loss: 0.04142614371246762
Epoch 10/10, Loss: 0.024347717847882047
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.83 1.00 0.90 19
Pipes 1.00 0.94 0.97 17
Rockets 1.00 0.82 0.90 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.93 70
macro avg 0.95 0.92 0.93 70
weighted avg 0.94 0.93 0.93 70
Training resnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3540249632464514
Epoch 2/10, Loss: 0.6230035589800941
Epoch 3/10, Loss: 0.30138611048460007
Epoch 4/10, Loss: 0.11659369783269034
Epoch 5/10, Loss: 0.06919514279191692
Epoch 6/10, Loss: 0.05876235322405895
Epoch 7/10, Loss: 0.024356902551112905
Epoch 8/10, Loss: 0.035866833861089416
Epoch 9/10, Loss: 0.032984534873523645
Epoch 10/10, Loss: 0.021831105437336698
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.75 0.79 0.77 19
Pipes 1.00 0.82 0.90 17
Rockets 0.69 0.82 0.75 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.83 70
macro avg 0.84 0.84 0.83 70
weighted avg 0.84 0.83 0.83 70
Training resnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3388982613881428
Epoch 2/10, Loss: 0.6537351707617441
Epoch 3/10, Loss: 0.259066351585918
Epoch 4/10, Loss: 0.14709110082023674
Epoch 5/10, Loss: 0.1055682719581657
Epoch 6/10, Loss: 0.07008134718570444
Epoch 7/10, Loss: 0.03903940485583411
Epoch 8/10, Loss: 0.06211975346215897
Epoch 9/10, Loss: 0.06239812872889969
Epoch 10/10, Loss: 0.05343457044930094
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.93 0.74 0.82 19
Pipes 0.76 0.94 0.84 17
Rockets 0.78 0.64 0.70 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.80 70
macro avg 0.80 0.79 0.79 70
weighted avg 0.81 0.80 0.80 70
Training resnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.2957803275850084
Epoch 2/10, Loss: 0.471657019522455
Epoch 3/10, Loss: 0.21108960401680735
Epoch 4/10, Loss: 0.10436863772985008
Epoch 5/10, Loss: 0.0854907627734873
Epoch 6/10, Loss: 0.03763633518893686
Epoch 7/10, Loss: 0.013226876500993967
Epoch 8/10, Loss: 0.03456657729111612
Epoch 9/10, Loss: 0.02067176365138342
Epoch 10/10, Loss: 0.05846775039875259
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 1.00 0.89 0.94 19
Pipes 1.00 0.94 0.97 17
Rockets 0.62 0.91 0.74 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.89 70
macro avg 0.89 0.88 0.88 70
weighted avg 0.91 0.89 0.89 70
Training resnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.251389308108224
Epoch 2/10, Loss: 0.5880393551455604
Epoch 3/10, Loss: 0.26790445380740696
Epoch 4/10, Loss: 0.1764424732989735
Epoch 5/10, Loss: 0.11330122221261263
Epoch 6/10, Loss: 0.09905826083074014
Epoch 7/10, Loss: 0.02900419808510277
Epoch 8/10, Loss: 0.05638750218268898
Epoch 9/10, Loss: 0.09867234445280498
Epoch 10/10, Loss: 0.09005661013846596
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.94 0.89 0.92 19
Pipes 1.00 0.94 0.97 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.91 70
macro avg 0.91 0.92 0.91 70
weighted avg 0.92 0.91 0.91 70
Training resnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3653299080000982
Epoch 2/10, Loss: 0.6806349837117724
Epoch 3/10, Loss: 0.3400388037165006
Epoch 4/10, Loss: 0.14956268833743203
Epoch 5/10, Loss: 0.12667082426034743
Epoch 6/10, Loss: 0.11949782332198487
Epoch 7/10, Loss: 0.07323208321920699
Epoch 8/10, Loss: 0.03755632498198085
Epoch 9/10, Loss: 0.04413966844893164
Epoch 10/10, Loss: 0.06876280202737285
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.94 0.84 0.89 19
Pipes 0.94 0.88 0.91 17
Rockets 0.77 0.91 0.83 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.87 70
macro avg 0.86 0.88 0.87 70
weighted avg 0.88 0.87 0.87 70
Training resnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.575971908039517
Epoch 2/10, Loss: 1.3114522298177083
Epoch 3/10, Loss: 0.9871027072270712
Epoch 4/10, Loss: 0.6289917230606079
Epoch 5/10, Loss: 0.33272578318913776
Epoch 6/10, Loss: 0.14961169163386026
Epoch 7/10, Loss: 0.0675371065735817
Epoch 8/10, Loss: 0.04311293839580483
Epoch 9/10, Loss: 0.025676831810010806
Epoch 10/10, Loss: 0.013937357709639601
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.90 0.95 0.92 19
Pipes 1.00 0.82 0.90 17
Rockets 0.71 0.91 0.80 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.87 70
macro avg 0.88 0.87 0.86 70
weighted avg 0.89 0.87 0.87 70
Training resnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.5815119345982869
Epoch 2/10, Loss: 1.3170017004013062
Epoch 3/10, Loss: 1.0349720319112141
Epoch 4/10, Loss: 0.6598896649148729
Epoch 5/10, Loss: 0.44105420841111076
Epoch 6/10, Loss: 0.194710878862275
Epoch 7/10, Loss: 0.08799890594349967
Epoch 8/10, Loss: 0.05097314053111606
Epoch 9/10, Loss: 0.03874476709299617
Epoch 10/10, Loss: 0.02007631419433488
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.77 0.89 0.83 19
Pipes 1.00 0.82 0.90 17
Rockets 0.70 0.64 0.67 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.81 70
macro avg 0.81 0.80 0.80 70
weighted avg 0.82 0.81 0.81 70
Training resnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.5688687695397272
Epoch 2/10, Loss: 1.3943485021591187
Epoch 3/10, Loss: 1.1342016458511353
Epoch 4/10, Loss: 0.8324056598875258
Epoch 5/10, Loss: 0.534795085589091
Epoch 6/10, Loss: 0.3050510783990224
Epoch 7/10, Loss: 0.1458032950758934
Epoch 8/10, Loss: 0.09041348016924328
Epoch 9/10, Loss: 0.05875698083804713
Epoch 10/10, Loss: 0.04446825405789746
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.82 0.82 0.82 11
Mines 0.90 0.95 0.92 19
Pipes 0.94 0.88 0.91 17
Rockets 0.71 0.91 0.80 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.86 70
macro avg 0.85 0.84 0.84 70
weighted avg 0.87 0.86 0.86 70
Training resnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5663804213205974
Epoch 2/10, Loss: 1.2828904390335083
Epoch 3/10, Loss: 0.9189309345351325
Epoch 4/10, Loss: 0.5554558833440145
Epoch 5/10, Loss: 0.28889671630329555
Epoch 6/10, Loss: 0.1621706841720475
Epoch 7/10, Loss: 0.0669093235499329
Epoch 8/10, Loss: 0.05271550619767772
Epoch 9/10, Loss: 0.02411248369349374
Epoch 10/10, Loss: 0.017445092606875632
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.73 1.00 0.84 19
Pipes 1.00 0.76 0.87 17
Rockets 0.83 0.91 0.87 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.86 70
macro avg 0.89 0.85 0.86 70
weighted avg 0.89 0.86 0.86 70
Training resnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5675266053941515
Epoch 2/10, Loss: 1.308297594388326
Epoch 3/10, Loss: 1.029781354798211
Epoch 4/10, Loss: 0.7019779748386807
Epoch 5/10, Loss: 0.418519741959042
Epoch 6/10, Loss: 0.21134930021233028
Epoch 7/10, Loss: 0.09964956839879353
Epoch 8/10, Loss: 0.05051411150230302
Epoch 9/10, Loss: 0.02459097984764311
Epoch 10/10, Loss: 0.02278050697512097
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.85 0.89 0.87 19
Pipes 1.00 0.88 0.94 17
Rockets 0.69 0.82 0.75 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.86 70
macro avg 0.86 0.85 0.85 70
weighted avg 0.87 0.86 0.86 70
Training resnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.587616867489285
Epoch 2/10, Loss: 1.4336976475185819
Epoch 3/10, Loss: 1.1923257774776883
Epoch 4/10, Loss: 0.9082709219720628
Epoch 5/10, Loss: 0.5911728739738464
Epoch 6/10, Loss: 0.34908409747812486
Epoch 7/10, Loss: 0.18583495252662235
Epoch 8/10, Loss: 0.09184198329846065
Epoch 9/10, Loss: 0.06590627713335885
Epoch 10/10, Loss: 0.03602327282230059
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.88 0.79 0.83 19
Pipes 1.00 0.88 0.94 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.86 70
macro avg 0.87 0.87 0.85 70
weighted avg 0.88 0.86 0.86 70
Training resnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5484876500235663
Epoch 2/10, Loss: 1.2302240795559354
Epoch 3/10, Loss: 0.8711420761214362
Epoch 4/10, Loss: 0.48336513174904716
Epoch 5/10, Loss: 0.23027324179808298
Epoch 6/10, Loss: 0.09844084249602424
Epoch 7/10, Loss: 0.04650586429569456
Epoch 8/10, Loss: 0.03057355930407842
Epoch 9/10, Loss: 0.017717200020949047
Epoch 10/10, Loss: 0.010885350768350892
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.85 0.89 0.87 19
Pipes 1.00 0.88 0.94 17
Rockets 0.82 0.82 0.82 11
Vehicles 0.80 0.67 0.73 12
accuracy 0.84 70
macro avg 0.84 0.83 0.83 70
weighted avg 0.85 0.84 0.84 70
Training resnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5554986927244399
Epoch 2/10, Loss: 1.275863528251648
Epoch 3/10, Loss: 0.9521858427259657
Epoch 4/10, Loss: 0.611458592944675
Epoch 5/10, Loss: 0.3284493370188607
Epoch 6/10, Loss: 0.16747315890259212
Epoch 7/10, Loss: 0.06842703910337554
Epoch 8/10, Loss: 0.04930761187440819
Epoch 9/10, Loss: 0.02199397888034582
Epoch 10/10, Loss: 0.02324554655287001
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.83 0.79 0.81 19
Pipes 0.88 0.88 0.88 17
Rockets 0.73 0.73 0.73 11
Vehicles 0.69 0.75 0.72 12
accuracy 0.81 70
macro avg 0.81 0.81 0.81 70
weighted avg 0.82 0.81 0.81 70
Training resnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6086411078770955
Epoch 2/10, Loss: 1.4316118293338351
Epoch 3/10, Loss: 1.241205996937222
Epoch 4/10, Loss: 0.9384413891368442
Epoch 5/10, Loss: 0.6610572139422098
Epoch 6/10, Loss: 0.3781992329491509
Epoch 7/10, Loss: 0.20789564318127102
Epoch 8/10, Loss: 0.09736657473776075
Epoch 9/10, Loss: 0.05170310868157281
Epoch 10/10, Loss: 0.03630821272316906
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.85 0.89 0.87 19
Pipes 0.93 0.82 0.88 17
Rockets 0.77 0.91 0.83 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.86 70
macro avg 0.86 0.86 0.85 70
weighted avg 0.87 0.86 0.86 70
Training resnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6246932480070326
Epoch 2/10, Loss: 1.6181264718373616
Epoch 3/10, Loss: 1.6015887392891779
Epoch 4/10, Loss: 1.5966008239322238
Epoch 5/10, Loss: 1.5936987664964464
Epoch 6/10, Loss: 1.5816749466790094
Epoch 7/10, Loss: 1.5688203308317397
Epoch 8/10, Loss: 1.5635114908218384
Epoch 9/10, Loss: 1.5615085893207126
Epoch 10/10, Loss: 1.5468322303560045
Accuracy: 34.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.37 0.91 0.53 11
Mines 0.31 0.47 0.38 19
Pipes 0.50 0.12 0.19 17
Rockets 0.33 0.09 0.14 11
Vehicles 0.29 0.17 0.21 12
accuracy 0.34 70
macro avg 0.36 0.35 0.29 70
weighted avg 0.37 0.34 0.29 70
Training resnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.600369069311354
Epoch 2/10, Loss: 1.5945000516043768
Epoch 3/10, Loss: 1.5908749368455675
Epoch 4/10, Loss: 1.5863232480155096
Epoch 5/10, Loss: 1.5712809695137873
Epoch 6/10, Loss: 1.5676860014597576
Epoch 7/10, Loss: 1.5568274921841092
Epoch 8/10, Loss: 1.5584859980477228
Epoch 9/10, Loss: 1.5320373906029596
Epoch 10/10, Loss: 1.5305529700385199
Accuracy: 25.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.45 0.45 0.45 11
Mines 0.00 0.00 0.00 19
Pipes 0.50 0.06 0.11 17
Rockets 0.25 0.18 0.21 11
Vehicles 0.23 0.83 0.36 12
accuracy 0.26 70
macro avg 0.29 0.31 0.23 70
weighted avg 0.27 0.26 0.19 70
Training resnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6101758347617254
Epoch 2/10, Loss: 1.5913859738243952
Epoch 3/10, Loss: 1.5942339367336698
Epoch 4/10, Loss: 1.5809625652101305
Epoch 5/10, Loss: 1.5686530007256403
Epoch 6/10, Loss: 1.5765582985348172
Epoch 7/10, Loss: 1.552019887500339
Epoch 8/10, Loss: 1.5400802824232314
Epoch 9/10, Loss: 1.5540304713779025
Epoch 10/10, Loss: 1.5194140010409884
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.25 0.09 0.13 11
Mines 0.32 0.37 0.34 19
Pipes 1.00 0.00 0.00 17
Rockets 0.20 0.09 0.12 11
Vehicles 0.26 0.83 0.39 12
accuracy 0.27 70
macro avg 0.40 0.28 0.20 70
weighted avg 0.44 0.27 0.20 70
Training resnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6369933287302654
Epoch 2/10, Loss: 1.6302740573883057
Epoch 3/10, Loss: 1.6261276139153376
Epoch 4/10, Loss: 1.6173714399337769
Epoch 5/10, Loss: 1.6040468878216214
Epoch 6/10, Loss: 1.599316464530097
Epoch 7/10, Loss: 1.593336409992642
Epoch 8/10, Loss: 1.5889347261852689
Epoch 9/10, Loss: 1.5763531525929768
Epoch 10/10, Loss: 1.5614031420813665
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.27 0.27 0.27 11
Mines 0.25 0.11 0.15 19
Pipes 0.33 0.12 0.17 17
Rockets 0.14 0.45 0.21 11
Vehicles 0.44 0.33 0.38 12
accuracy 0.23 70
macro avg 0.29 0.26 0.24 70
weighted avg 0.29 0.23 0.22 70
Training resnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.606213026576572
Epoch 2/10, Loss: 1.5979855325486925
Epoch 3/10, Loss: 1.592103123664856
Epoch 4/10, Loss: 1.586882750193278
Epoch 5/10, Loss: 1.5799590746561687
Epoch 6/10, Loss: 1.5687200493282742
Epoch 7/10, Loss: 1.5631745391421847
Epoch 8/10, Loss: 1.5472636487748888
Epoch 9/10, Loss: 1.553866399659051
Epoch 10/10, Loss: 1.5271068678961859
Accuracy: 18.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.25 0.36 0.30 11
Mines 0.14 0.11 0.12 19
Pipes 1.00 0.00 0.00 17
Rockets 0.31 0.36 0.33 11
Vehicles 0.11 0.25 0.15 12
accuracy 0.19 70
macro avg 0.36 0.22 0.18 70
weighted avg 0.39 0.19 0.16 70
Training resnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.617892821629842
Epoch 2/10, Loss: 1.6361271142959595
Epoch 3/10, Loss: 1.6317211257086859
Epoch 4/10, Loss: 1.6112473011016846
Epoch 5/10, Loss: 1.614691350195143
Epoch 6/10, Loss: 1.5902399751875136
Epoch 7/10, Loss: 1.5956109232372708
Epoch 8/10, Loss: 1.5800676080915663
Epoch 9/10, Loss: 1.5643117030461628
Epoch 10/10, Loss: 1.5835203727086384
Accuracy: 18.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.25 0.18 0.21 11
Mines 0.25 0.11 0.15 19
Pipes 0.21 0.18 0.19 17
Rockets 0.06 0.09 0.07 11
Vehicles 0.22 0.42 0.29 12
accuracy 0.19 70
macro avg 0.20 0.19 0.18 70
weighted avg 0.21 0.19 0.18 70
Training resnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6169826189676921
Epoch 2/10, Loss: 1.6197213994132147
Epoch 3/10, Loss: 1.6023457182778253
Epoch 4/10, Loss: 1.5846798287497625
Epoch 5/10, Loss: 1.5837531222237482
Epoch 6/10, Loss: 1.5838357739978366
Epoch 7/10, Loss: 1.5721960994932387
Epoch 8/10, Loss: 1.559683124224345
Epoch 9/10, Loss: 1.542986234029134
Epoch 10/10, Loss: 1.535590648651123
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.60 0.27 0.38 11
Mines 0.50 0.16 0.24 19
Pipes 1.00 0.00 0.00 17
Rockets 0.50 0.09 0.15 11
Vehicles 0.21 1.00 0.35 12
accuracy 0.27 70
macro avg 0.56 0.30 0.22 70
weighted avg 0.59 0.27 0.21 70
Training resnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6247356202867296
Epoch 2/10, Loss: 1.6039797465006511
Epoch 3/10, Loss: 1.61067975891961
Epoch 4/10, Loss: 1.5940727260377672
Epoch 5/10, Loss: 1.581650389565362
Epoch 6/10, Loss: 1.5806729131274753
Epoch 7/10, Loss: 1.5727689928478665
Epoch 8/10, Loss: 1.5583977037005954
Epoch 9/10, Loss: 1.5669348239898682
Epoch 10/10, Loss: 1.5492290258407593
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.05 0.10 19
Pipes 0.60 0.18 0.27 17
Rockets 0.15 0.55 0.24 11
Vehicles 0.28 0.58 0.38 12
accuracy 0.24 70
macro avg 0.61 0.27 0.20 70
weighted avg 0.65 0.24 0.20 70
Training resnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6236917972564697
Epoch 2/10, Loss: 1.6131466627120972
Epoch 3/10, Loss: 1.6069249312082927
Epoch 4/10, Loss: 1.605391091770596
Epoch 5/10, Loss: 1.5931553973091974
Epoch 6/10, Loss: 1.5821436246236165
Epoch 7/10, Loss: 1.5924721558888753
Epoch 8/10, Loss: 1.575030552016364
Epoch 9/10, Loss: 1.5583094358444214
Epoch 10/10, Loss: 1.5637943612204657
Accuracy: 30.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.25 0.18 0.21 11
Mines 0.30 0.37 0.33 19
Pipes 0.14 0.06 0.08 17
Rockets 0.31 0.36 0.33 11
Vehicles 0.37 0.58 0.45 12
accuracy 0.30 70
macro avg 0.27 0.31 0.28 70
weighted avg 0.27 0.30 0.27 70
Training resnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.3136902981334262
Epoch 2/10, Loss: 0.4983719355530209
Epoch 3/10, Loss: 0.15109425865941578
Epoch 4/10, Loss: 0.043648474746280246
Epoch 5/10, Loss: 0.023713140282779932
Epoch 6/10, Loss: 0.011522860835409827
Epoch 7/10, Loss: 0.009905304294079542
Epoch 8/10, Loss: 0.009557019039574597
Epoch 9/10, Loss: 0.045777335556017026
Epoch 10/10, Loss: 0.013962322354523672
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.79 0.79 0.79 19
Pipes 0.88 0.82 0.85 17
Rockets 0.90 0.82 0.86 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.83 70
macro avg 0.84 0.84 0.83 70
weighted avg 0.84 0.83 0.83 70
Training resnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.3705554405848186
Epoch 2/10, Loss: 0.4887884027428097
Epoch 3/10, Loss: 0.1610555379754967
Epoch 4/10, Loss: 0.06603939810560809
Epoch 5/10, Loss: 0.014807204405466715
Epoch 6/10, Loss: 0.010645891022351053
Epoch 7/10, Loss: 0.00919506476364202
Epoch 8/10, Loss: 0.015414022245547838
Epoch 9/10, Loss: 0.009446364568753375
Epoch 10/10, Loss: 0.004510116323621737
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.90 0.95 0.92 19
Pipes 1.00 0.94 0.97 17
Rockets 0.77 0.91 0.83 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.90 70
macro avg 0.90 0.89 0.89 70
weighted avg 0.91 0.90 0.90 70
Training resnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.4216499593522813
Epoch 2/10, Loss: 0.7009072237544589
Epoch 3/10, Loss: 0.20553181734349993
Epoch 4/10, Loss: 0.07850356772542
Epoch 5/10, Loss: 0.038507610766424075
Epoch 6/10, Loss: 0.015736215333971713
Epoch 7/10, Loss: 0.018273480081309874
Epoch 8/10, Loss: 0.007051437446433637
Epoch 9/10, Loss: 0.006444531855069929
Epoch 10/10, Loss: 0.007696918470578061
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.79 0.79 0.79 19
Pipes 0.94 0.88 0.91 17
Rockets 0.80 0.73 0.76 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.84 70
macro avg 0.84 0.85 0.84 70
weighted avg 0.84 0.84 0.84 70
Training resnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.3213494618733723
Epoch 2/10, Loss: 0.4201981590853797
Epoch 3/10, Loss: 0.13741070280472437
Epoch 4/10, Loss: 0.08484691236582068
Epoch 5/10, Loss: 0.03979972284287214
Epoch 6/10, Loss: 0.013206731838484606
Epoch 7/10, Loss: 0.011991715213904778
Epoch 8/10, Loss: 0.006257815286517143
Epoch 9/10, Loss: 0.007526550933511721
Epoch 10/10, Loss: 0.05650032560030619
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.89 0.89 0.89 19
Pipes 0.94 0.88 0.91 17
Rockets 0.77 0.91 0.83 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.87 70
macro avg 0.87 0.87 0.87 70
weighted avg 0.88 0.87 0.87 70
Training resnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3423397143681843
Epoch 2/10, Loss: 0.5642099413606856
Epoch 3/10, Loss: 0.23167482018470764
Epoch 4/10, Loss: 0.09825648284620708
Epoch 5/10, Loss: 0.028526427534719307
Epoch 6/10, Loss: 0.01658626226708293
Epoch 7/10, Loss: 0.010774528245545097
Epoch 8/10, Loss: 0.008192996261641383
Epoch 9/10, Loss: 0.011631380792500244
Epoch 10/10, Loss: 0.007281785960205727
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.85 0.89 0.87 19
Pipes 0.94 0.88 0.91 17
Rockets 0.91 0.91 0.91 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.89 70
macro avg 0.89 0.89 0.89 70
weighted avg 0.89 0.89 0.89 70
Training resnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3536057339774237
Epoch 2/10, Loss: 0.6321179237630632
Epoch 3/10, Loss: 0.21001139283180237
Epoch 4/10, Loss: 0.08957884750432438
Epoch 5/10, Loss: 0.052880524140265256
Epoch 6/10, Loss: 0.016241217859917216
Epoch 7/10, Loss: 0.015714208285013836
Epoch 8/10, Loss: 0.008047157484624121
Epoch 9/10, Loss: 0.016724566023589835
Epoch 10/10, Loss: 0.029187203074495
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.89 0.89 0.89 19
Pipes 0.82 0.82 0.82 17
Rockets 0.67 0.55 0.60 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.80 70
macro avg 0.79 0.78 0.78 70
weighted avg 0.81 0.80 0.80 70
Training resnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.2652963333659701
Epoch 2/10, Loss: 0.4504602551460266
Epoch 3/10, Loss: 0.22518679665194619
Epoch 4/10, Loss: 0.06911410784555806
Epoch 5/10, Loss: 0.03989859504832162
Epoch 6/10, Loss: 0.030326390431986913
Epoch 7/10, Loss: 0.006980256177484989
Epoch 8/10, Loss: 0.006465134029794071
Epoch 9/10, Loss: 0.007965676952153444
Epoch 10/10, Loss: 0.0045891085432635415
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.94 0.89 0.92 19
Pipes 0.94 0.94 0.94 17
Rockets 0.77 0.91 0.83 11
Vehicles 0.80 0.67 0.73 12
accuracy 0.87 70
macro avg 0.86 0.86 0.86 70
weighted avg 0.87 0.87 0.87 70
Training resnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.4002746476067438
Epoch 2/10, Loss: 0.5913276573022207
Epoch 3/10, Loss: 0.15674855849809116
Epoch 4/10, Loss: 0.037886120585931674
Epoch 5/10, Loss: 0.016007072085307703
Epoch 6/10, Loss: 0.015062725119706657
Epoch 7/10, Loss: 0.008589373642785681
Epoch 8/10, Loss: 0.007803514677410324
Epoch 9/10, Loss: 0.004502057227202588
Epoch 10/10, Loss: 0.004917793855484989
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.94 0.79 0.86 19
Pipes 0.93 0.76 0.84 17
Rockets 0.56 0.91 0.69 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.81 70
macro avg 0.83 0.82 0.81 70
weighted avg 0.85 0.81 0.82 70
Training resnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.4048510657416449
Epoch 2/10, Loss: 0.6402730478180779
Epoch 3/10, Loss: 0.2063226741221216
Epoch 4/10, Loss: 0.1050531454384327
Epoch 5/10, Loss: 0.0460448521706793
Epoch 6/10, Loss: 0.04368932069175773
Epoch 7/10, Loss: 0.01533581678652101
Epoch 8/10, Loss: 0.013568307376570173
Epoch 9/10, Loss: 0.017208934451142948
Epoch 10/10, Loss: 0.007805378952374061
Accuracy: 78.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.61 1.00 0.76 11
Mines 0.87 0.68 0.76 19
Pipes 0.79 0.88 0.83 17
Rockets 0.88 0.64 0.74 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.79 70
macro avg 0.81 0.79 0.78 70
weighted avg 0.81 0.79 0.79 70
Training resnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.588121771812439
Epoch 2/10, Loss: 1.3558692932128906
Epoch 3/10, Loss: 1.1108723402023315
Epoch 4/10, Loss: 0.8153022527694702
Epoch 5/10, Loss: 0.5720710992813111
Epoch 6/10, Loss: 0.3794432520866394
Epoch 7/10, Loss: 0.24470545947551728
Epoch 8/10, Loss: 0.10199617892503739
Epoch 9/10, Loss: 0.06756700053811074
Epoch 10/10, Loss: 0.03745370618999004
Accuracy: 78.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.83 0.79 0.81 19
Pipes 1.00 0.82 0.90 17
Rockets 0.55 0.55 0.55 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.79 70
macro avg 0.77 0.78 0.77 70
weighted avg 0.80 0.79 0.79 70
Training resnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.5902607679367065
Epoch 2/10, Loss: 1.390233039855957
Epoch 3/10, Loss: 1.1555104017257691
Epoch 4/10, Loss: 0.9224817991256714
Epoch 5/10, Loss: 0.721170163154602
Epoch 6/10, Loss: 0.5138000607490539
Epoch 7/10, Loss: 0.35348941683769225
Epoch 8/10, Loss: 0.19094848036766052
Epoch 9/10, Loss: 0.12703354358673097
Epoch 10/10, Loss: 0.08192578554153443
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.82 0.62 11
Mines 0.75 0.63 0.69 19
Pipes 0.88 0.88 0.88 17
Rockets 0.86 0.55 0.67 11
Vehicles 0.67 0.67 0.67 12
accuracy 0.71 70
macro avg 0.73 0.71 0.70 70
weighted avg 0.75 0.71 0.72 70
Training resnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.593225908279419
Epoch 2/10, Loss: 1.4172884225845337
Epoch 3/10, Loss: 1.2278281927108765
Epoch 4/10, Loss: 0.9923304915428162
Epoch 5/10, Loss: 0.7932725191116333
Epoch 6/10, Loss: 0.5728617429733276
Epoch 7/10, Loss: 0.41608489751815797
Epoch 8/10, Loss: 0.2894639134407043
Epoch 9/10, Loss: 0.17381275594234466
Epoch 10/10, Loss: 0.13654129952192307
Accuracy: 67.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.67 0.74 0.70 19
Pipes 1.00 0.59 0.74 17
Rockets 0.53 0.82 0.64 11
Vehicles 0.57 0.33 0.42 12
accuracy 0.67 70
macro avg 0.69 0.68 0.65 70
weighted avg 0.71 0.67 0.66 70
Training resnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5685826063156127
Epoch 2/10, Loss: 1.3102898120880127
Epoch 3/10, Loss: 1.00567067861557
Epoch 4/10, Loss: 0.7237242341041565
Epoch 5/10, Loss: 0.48790913820266724
Epoch 6/10, Loss: 0.259287616610527
Epoch 7/10, Loss: 0.14070359319448472
Epoch 8/10, Loss: 0.09302956908941269
Epoch 9/10, Loss: 0.04155474826693535
Epoch 10/10, Loss: 0.03568922989070415
Accuracy: 72.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.62 0.79 0.70 19
Pipes 1.00 0.71 0.83 17
Rockets 0.64 0.82 0.72 11
Vehicles 1.00 0.42 0.59 12
accuracy 0.73 70
macro avg 0.79 0.73 0.72 70
weighted avg 0.79 0.73 0.73 70
Training resnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5851529121398926
Epoch 2/10, Loss: 1.4175215244293213
Epoch 3/10, Loss: 1.2643432855606078
Epoch 4/10, Loss: 1.0283705115318298
Epoch 5/10, Loss: 0.7984218835830689
Epoch 6/10, Loss: 0.5817060232162475
Epoch 7/10, Loss: 0.39651567339897154
Epoch 8/10, Loss: 0.25066035985946655
Epoch 9/10, Loss: 0.15364557057619094
Epoch 10/10, Loss: 0.09995709955692292
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.76 0.68 0.72 19
Pipes 1.00 0.53 0.69 17
Rockets 0.60 0.82 0.69 11
Vehicles 0.53 0.67 0.59 12
accuracy 0.70 70
macro avg 0.72 0.72 0.70 70
weighted avg 0.75 0.70 0.70 70
Training resnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5912731409072876
Epoch 2/10, Loss: 1.4431364059448242
Epoch 3/10, Loss: 1.265181875228882
Epoch 4/10, Loss: 1.045304524898529
Epoch 5/10, Loss: 0.8935542225837707
Epoch 6/10, Loss: 0.6881312012672425
Epoch 7/10, Loss: 0.5109856963157654
Epoch 8/10, Loss: 0.3609282910823822
Epoch 9/10, Loss: 0.2648025661706924
Epoch 10/10, Loss: 0.1449626937508583
Accuracy: 68.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.55 1.00 0.71 11
Mines 0.68 0.68 0.68 19
Pipes 1.00 0.59 0.74 17
Rockets 0.62 0.73 0.67 11
Vehicles 0.75 0.50 0.60 12
accuracy 0.69 70
macro avg 0.72 0.70 0.68 70
weighted avg 0.74 0.69 0.68 70
Training resnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5766899108886718
Epoch 2/10, Loss: 1.3757041215896606
Epoch 3/10, Loss: 1.14422709941864
Epoch 4/10, Loss: 0.8762933611869812
Epoch 5/10, Loss: 0.6350632667541504
Epoch 6/10, Loss: 0.41650023460388186
Epoch 7/10, Loss: 0.23969777524471284
Epoch 8/10, Loss: 0.14138049483299256
Epoch 9/10, Loss: 0.0713344782590866
Epoch 10/10, Loss: 0.043458839505910875
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.82 0.78 11
Mines 0.70 0.84 0.76 19
Pipes 1.00 0.76 0.87 17
Rockets 0.69 0.82 0.75 11
Vehicles 0.78 0.58 0.67 12
accuracy 0.77 70
macro avg 0.78 0.77 0.77 70
weighted avg 0.79 0.77 0.77 70
Training resnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5995361328125
Epoch 2/10, Loss: 1.4300950765609741
Epoch 3/10, Loss: 1.2238139152526855
Epoch 4/10, Loss: 0.9832393527030945
Epoch 5/10, Loss: 0.7801450014114379
Epoch 6/10, Loss: 0.5346178889274598
Epoch 7/10, Loss: 0.36114081740379333
Epoch 8/10, Loss: 0.2330882579088211
Epoch 9/10, Loss: 0.1407115191221237
Epoch 10/10, Loss: 0.09045428633689881
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.82 0.82 0.82 11
Mines 0.76 0.84 0.80 19
Pipes 0.90 0.53 0.67 17
Rockets 0.54 0.64 0.58 11
Vehicles 0.60 0.75 0.67 12
accuracy 0.71 70
macro avg 0.72 0.72 0.71 70
weighted avg 0.74 0.71 0.71 70
Training resnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.571104598045349
Epoch 2/10, Loss: 1.3994359731674195
Epoch 3/10, Loss: 1.2173974990844727
Epoch 4/10, Loss: 1.0034225583076477
Epoch 5/10, Loss: 0.7967332005500793
Epoch 6/10, Loss: 0.5594799399375916
Epoch 7/10, Loss: 0.3721969187259674
Epoch 8/10, Loss: 0.2442086786031723
Epoch 9/10, Loss: 0.1786263793706894
Epoch 10/10, Loss: 0.10526305139064789
Accuracy: 68.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.60 0.63 0.62 19
Pipes 1.00 0.71 0.83 17
Rockets 0.50 0.45 0.48 11
Vehicles 0.56 0.75 0.64 12
accuracy 0.69 70
macro avg 0.70 0.69 0.69 70
weighted avg 0.71 0.69 0.69 70
Training resnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6252549886703491
Epoch 2/10, Loss: 1.6152791261672974
Epoch 3/10, Loss: 1.6177562713623046
Epoch 4/10, Loss: 1.6073922395706177
Epoch 5/10, Loss: 1.6123656511306763
Epoch 6/10, Loss: 1.5995468616485595
Epoch 7/10, Loss: 1.591336154937744
Epoch 8/10, Loss: 1.5883979797363281
Epoch 9/10, Loss: 1.5860807657241822
Epoch 10/10, Loss: 1.5773738622665405
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.00 0.00 0.00 11
Mines 0.23 0.16 0.19 19
Pipes 0.12 0.06 0.08 17
Rockets 0.11 0.27 0.15 11
Vehicles 0.20 0.33 0.25 12
accuracy 0.16 70
macro avg 0.13 0.16 0.13 70
weighted avg 0.14 0.16 0.14 70
Training resnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6156269788742066
Epoch 2/10, Loss: 1.6099807977676392
Epoch 3/10, Loss: 1.6092575788497925
Epoch 4/10, Loss: 1.6083696603775024
Epoch 5/10, Loss: 1.6081549882888795
Epoch 6/10, Loss: 1.5905154943466187
Epoch 7/10, Loss: 1.5853132009506226
Epoch 8/10, Loss: 1.5855697393417358
Epoch 9/10, Loss: 1.5738352537155151
Epoch 10/10, Loss: 1.5708152294158935
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.00 0.00 0.00 11
Mines 0.38 0.16 0.22 19
Pipes 0.67 0.12 0.20 17
Rockets 0.38 0.27 0.32 11
Vehicles 0.27 0.92 0.42 12
accuracy 0.27 70
macro avg 0.34 0.29 0.23 70
weighted avg 0.37 0.27 0.23 70
Training resnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6093427419662476
Epoch 2/10, Loss: 1.6223686695098878
Epoch 3/10, Loss: 1.6094569444656373
Epoch 4/10, Loss: 1.5967779397964477
Epoch 5/10, Loss: 1.5848189353942872
Epoch 6/10, Loss: 1.5890814542770386
Epoch 7/10, Loss: 1.586869192123413
Epoch 8/10, Loss: 1.5691256761550902
Epoch 9/10, Loss: 1.5652189016342164
Epoch 10/10, Loss: 1.5764300346374511
Accuracy: 30.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.09 0.15 11
Mines 0.18 0.11 0.13 19
Pipes 0.50 0.29 0.37 17
Rockets 0.28 0.45 0.34 11
Vehicles 0.28 0.67 0.39 12
accuracy 0.30 70
macro avg 0.35 0.32 0.28 70
weighted avg 0.34 0.30 0.27 70
Training resnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6044719457626342
Epoch 2/10, Loss: 1.6101480007171631
Epoch 3/10, Loss: 1.6065105676651001
Epoch 4/10, Loss: 1.5974714040756226
Epoch 5/10, Loss: 1.591896915435791
Epoch 6/10, Loss: 1.6012208938598633
Epoch 7/10, Loss: 1.599009394645691
Epoch 8/10, Loss: 1.5846624612808227
Epoch 9/10, Loss: 1.587524700164795
Epoch 10/10, Loss: 1.5794912815093993
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.00 0.00 0.00 11
Mines 0.33 0.26 0.29 19
Pipes 0.56 0.29 0.38 17
Rockets 0.15 0.27 0.19 11
Vehicles 0.18 0.33 0.24 12
accuracy 0.24 70
macro avg 0.24 0.23 0.22 70
weighted avg 0.28 0.24 0.24 70
Training resnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6331548929214477
Epoch 2/10, Loss: 1.6233587741851807
Epoch 3/10, Loss: 1.62376971244812
Epoch 4/10, Loss: 1.6088922023773193
Epoch 5/10, Loss: 1.6135443925857544
Epoch 6/10, Loss: 1.6111511945724488
Epoch 7/10, Loss: 1.594441556930542
Epoch 8/10, Loss: 1.5868095397949218
Epoch 9/10, Loss: 1.5858991861343383
Epoch 10/10, Loss: 1.5766668558120727
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.00 0.00 0.00 11
Mines 0.00 0.00 0.00 19
Pipes 0.50 0.47 0.48 17
Rockets 0.22 0.64 0.33 11
Vehicles 0.22 0.33 0.27 12
accuracy 0.27 70
macro avg 0.19 0.29 0.22 70
weighted avg 0.19 0.27 0.21 70
Training resnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6276986360549928
Epoch 2/10, Loss: 1.6237280368804932
Epoch 3/10, Loss: 1.6096614360809327
Epoch 4/10, Loss: 1.6095125436782838
Epoch 5/10, Loss: 1.5922738790512085
Epoch 6/10, Loss: 1.603747010231018
Epoch 7/10, Loss: 1.6058130264282227
Epoch 8/10, Loss: 1.6049516439437865
Epoch 9/10, Loss: 1.5863852739334106
Epoch 10/10, Loss: 1.5751615047454834
Accuracy: 20.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.22 0.45 0.29 11
Mines 0.40 0.11 0.17 19
Pipes 0.00 0.00 0.00 17
Rockets 0.23 0.45 0.30 11
Vehicles 0.12 0.17 0.14 12
accuracy 0.20 70
macro avg 0.19 0.24 0.18 70
weighted avg 0.20 0.20 0.16 70
Training resnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6049991846084595
Epoch 2/10, Loss: 1.6067051649093629
Epoch 3/10, Loss: 1.6070804357528687
Epoch 4/10, Loss: 1.5895148038864135
Epoch 5/10, Loss: 1.5831661939620971
Epoch 6/10, Loss: 1.5825759410858153
Epoch 7/10, Loss: 1.5819961547851562
Epoch 8/10, Loss: 1.567879891395569
Epoch 9/10, Loss: 1.5600414037704469
Epoch 10/10, Loss: 1.5556689023971557
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.24 0.64 0.35 11
Mines 0.40 0.11 0.17 19
Pipes 1.00 0.18 0.30 17
Rockets 0.12 0.09 0.11 11
Vehicles 0.12 0.25 0.16 12
accuracy 0.23 70
macro avg 0.38 0.25 0.22 70
weighted avg 0.43 0.23 0.22 70
Training resnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6149425029754638
Epoch 2/10, Loss: 1.5996164798736572
Epoch 3/10, Loss: 1.5999584436416625
Epoch 4/10, Loss: 1.6062180757522584
Epoch 5/10, Loss: 1.590840482711792
Epoch 6/10, Loss: 1.5821696758270263
Epoch 7/10, Loss: 1.5839499711990357
Epoch 8/10, Loss: 1.570285439491272
Epoch 9/10, Loss: 1.562325406074524
Epoch 10/10, Loss: 1.559882640838623
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.29 0.64 0.40 11
Mines 0.33 0.11 0.16 19
Pipes 0.75 0.18 0.29 17
Rockets 0.13 0.18 0.15 11
Vehicles 0.24 0.42 0.30 12
accuracy 0.27 70
macro avg 0.35 0.30 0.26 70
weighted avg 0.38 0.27 0.25 70
Training resnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6382278442382812
Epoch 2/10, Loss: 1.6211819171905517
Epoch 3/10, Loss: 1.628009819984436
Epoch 4/10, Loss: 1.6230915307998657
Epoch 5/10, Loss: 1.613485860824585
Epoch 6/10, Loss: 1.6137868165969849
Epoch 7/10, Loss: 1.596460747718811
Epoch 8/10, Loss: 1.6013261318206786
Epoch 9/10, Loss: 1.5870519161224366
Epoch 10/10, Loss: 1.5881022214889526
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.00 0.00 0.00 11
Mines 0.28 0.37 0.32 19
Pipes 0.36 0.29 0.32 17
Rockets 0.36 0.45 0.40 11
Vehicles 0.00 0.00 0.00 12
accuracy 0.24 70
macro avg 0.20 0.22 0.21 70
weighted avg 0.22 0.24 0.23 70
Training resnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.3255510568618774
Epoch 2/10, Loss: 0.5509988963603973
Epoch 3/10, Loss: 0.25223916471004487
Epoch 4/10, Loss: 0.05227530375123024
Epoch 5/10, Loss: 0.034011542424559595
Epoch 6/10, Loss: 0.022306686267256737
Epoch 7/10, Loss: 0.02053233068436384
Epoch 8/10, Loss: 0.007818875554949044
Epoch 9/10, Loss: 0.0072649320587515834
Epoch 10/10, Loss: 0.0038101875223219396
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.89 0.89 0.89 19
Pipes 1.00 0.82 0.90 17
Rockets 0.75 0.82 0.78 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.86 70
macro avg 0.86 0.86 0.85 70
weighted avg 0.87 0.86 0.86 70
Training resnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.4665767192840575
Epoch 2/10, Loss: 0.7180931985378265
Epoch 3/10, Loss: 0.23901735842227936
Epoch 4/10, Loss: 0.0854849360883236
Epoch 5/10, Loss: 0.03769069798290729
Epoch 6/10, Loss: 0.029638371989130975
Epoch 7/10, Loss: 0.016862634383141995
Epoch 8/10, Loss: 0.019216708745807408
Epoch 9/10, Loss: 0.015860540419816972
Epoch 10/10, Loss: 0.005491557344794273
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.77 0.89 0.83 19
Pipes 1.00 0.88 0.94 17
Rockets 0.67 0.73 0.70 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.83 70
macro avg 0.84 0.82 0.82 70
weighted avg 0.85 0.83 0.83 70
Training resnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.4608987808227538
Epoch 2/10, Loss: 0.9187035202980042
Epoch 3/10, Loss: 0.37449208498001096
Epoch 4/10, Loss: 0.11280890703201293
Epoch 5/10, Loss: 0.034892798587679866
Epoch 6/10, Loss: 0.019761212915182114
Epoch 7/10, Loss: 0.04240489583462477
Epoch 8/10, Loss: 0.022470065020024778
Epoch 9/10, Loss: 0.027262480184435844
Epoch 10/10, Loss: 0.012047452200204135
Accuracy: 78.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.89 0.73 0.80 11
Mines 0.63 1.00 0.78 19
Pipes 1.00 0.65 0.79 17
Rockets 0.78 0.64 0.70 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.79 70
macro avg 0.84 0.77 0.79 70
weighted avg 0.83 0.79 0.79 70
Training resnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.3598609685897827
Epoch 2/10, Loss: 0.5697896599769592
Epoch 3/10, Loss: 0.23460719883441924
Epoch 4/10, Loss: 0.06700490862131118
Epoch 5/10, Loss: 0.02145538218319416
Epoch 6/10, Loss: 0.010895036440342665
Epoch 7/10, Loss: 0.0071750650182366375
Epoch 8/10, Loss: 0.0057587024290114645
Epoch 9/10, Loss: 0.004305248241871595
Epoch 10/10, Loss: 0.0035249394131824374
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.77 0.89 0.83 19
Pipes 1.00 0.82 0.90 17
Rockets 0.82 0.82 0.82 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.83 70
macro avg 0.84 0.82 0.82 70
weighted avg 0.85 0.83 0.83 70
Training resnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.4577643871307373
Epoch 2/10, Loss: 0.7202398598194122
Epoch 3/10, Loss: 0.2541679948568344
Epoch 4/10, Loss: 0.10747993513941764
Epoch 5/10, Loss: 0.04089572690427303
Epoch 6/10, Loss: 0.01966143902391195
Epoch 7/10, Loss: 0.015404397621750832
Epoch 8/10, Loss: 0.07260122876614332
Epoch 9/10, Loss: 0.05701989606022835
Epoch 10/10, Loss: 0.007882081344723702
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.86 0.95 0.90 19
Pipes 1.00 0.82 0.90 17
Rockets 0.67 0.73 0.70 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.84 70
macro avg 0.84 0.83 0.83 70
weighted avg 0.86 0.84 0.84 70
Training resnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.4322444915771484
Epoch 2/10, Loss: 0.8018463850021362
Epoch 3/10, Loss: 0.3293684422969818
Epoch 4/10, Loss: 0.10769243240356445
Epoch 5/10, Loss: 0.04376395270228386
Epoch 6/10, Loss: 0.025377642177045346
Epoch 7/10, Loss: 0.014839136600494384
Epoch 8/10, Loss: 0.01688258145004511
Epoch 9/10, Loss: 0.010190125834196807
Epoch 10/10, Loss: 0.005198546778410673
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.94 0.84 0.89 19
Pipes 0.94 0.88 0.91 17
Rockets 0.62 0.91 0.74 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.84 70
macro avg 0.86 0.84 0.83 70
weighted avg 0.88 0.84 0.84 70
Training resnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.3921364545822144
Epoch 2/10, Loss: 0.59040407538414
Epoch 3/10, Loss: 0.18939270824193954
Epoch 4/10, Loss: 0.1164669930934906
Epoch 5/10, Loss: 0.08095981106162072
Epoch 6/10, Loss: 0.022270596399903298
Epoch 7/10, Loss: 0.012587666697800159
Epoch 8/10, Loss: 0.015070964582264423
Epoch 9/10, Loss: 0.008398691006004811
Epoch 10/10, Loss: 0.0031996523030102253
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 1.00 0.63 0.77 19
Pipes 0.70 0.94 0.80 17
Rockets 0.69 0.82 0.75 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.77 70
macro avg 0.81 0.78 0.77 70
weighted avg 0.83 0.77 0.77 70
Training resnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.4493956804275512
Epoch 2/10, Loss: 0.6590904533863068
Epoch 3/10, Loss: 0.24970228970050812
Epoch 4/10, Loss: 0.06230421438813209
Epoch 5/10, Loss: 0.025620002299547195
Epoch 6/10, Loss: 0.01016398323699832
Epoch 7/10, Loss: 0.010637633875012398
Epoch 8/10, Loss: 0.005569538054987788
Epoch 9/10, Loss: 0.014951829332858324
Epoch 10/10, Loss: 0.07832517698407174
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.88 0.79 0.83 19
Pipes 0.94 0.94 0.94 17
Rockets 0.71 0.91 0.80 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.84 70
macro avg 0.85 0.84 0.83 70
weighted avg 0.87 0.84 0.84 70
Training resnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.4397478342056274
Epoch 2/10, Loss: 0.8003571987152099
Epoch 3/10, Loss: 0.4241487383842468
Epoch 4/10, Loss: 0.25005241483449936
Epoch 5/10, Loss: 0.07740218564867973
Epoch 6/10, Loss: 0.05346528701484203
Epoch 7/10, Loss: 0.020518455281853677
Epoch 8/10, Loss: 0.0226755253970623
Epoch 9/10, Loss: 0.010916072130203246
Epoch 10/10, Loss: 0.010954734589904546
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.90 0.95 0.92 19
Pipes 1.00 0.82 0.90 17
Rockets 0.71 0.91 0.80 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.86 70
macro avg 0.87 0.85 0.84 70
weighted avg 0.89 0.86 0.85 70
Training vgg with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.938179201549954
Epoch 2/10, Loss: 2.0797112186749778
Epoch 3/10, Loss: 1.6204351517889235
Epoch 4/10, Loss: 1.6242321795887418
Epoch 5/10, Loss: 1.504336463080512
Epoch 6/10, Loss: 1.7119739121860928
Epoch 7/10, Loss: 1.3788148661454518
Epoch 8/10, Loss: 1.3630014657974243
Epoch 9/10, Loss: 1.3893239233228896
Epoch 10/10, Loss: 1.322395430670844
Accuracy: 30.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.33 0.47 0.39 19
Pipes 1.00 0.00 0.00 17
Rockets 0.24 0.36 0.29 11
Vehicles 0.31 0.67 0.42 12
accuracy 0.30 70
macro avg 0.58 0.30 0.22 70
weighted avg 0.58 0.30 0.22 70
Training vgg with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.8733841247028775
Epoch 2/10, Loss: 1.6951455142762926
Epoch 3/10, Loss: 1.6212145818604364
Epoch 4/10, Loss: 1.6250087883737352
Epoch 5/10, Loss: 1.471630573272705
Epoch 6/10, Loss: 1.4713760548167758
Epoch 7/10, Loss: 1.4032471312416925
Epoch 8/10, Loss: 1.4386487868097093
Epoch 9/10, Loss: 1.2940483457512326
Epoch 10/10, Loss: 1.3821894394026861
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.23 0.64 0.33 11
Vehicles 0.26 0.83 0.39 12
accuracy 0.24 70
macro avg 0.70 0.29 0.15 70
weighted avg 0.75 0.24 0.12 70
Training vgg with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.7976986765861511
Epoch 2/10, Loss: 1.6683068142996893
Epoch 3/10, Loss: 1.6011799706353083
Epoch 4/10, Loss: 1.5407189461919997
Epoch 5/10, Loss: 1.472961015171475
Epoch 6/10, Loss: 1.3764551215701633
Epoch 7/10, Loss: 1.2598197360833485
Epoch 8/10, Loss: 1.2238866885503132
Epoch 9/10, Loss: 1.3070544534259372
Epoch 10/10, Loss: 1.7622696492407057
Accuracy: 18.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.22 0.45 0.29 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.21 0.64 0.32 11
Vehicles 0.07 0.08 0.08 12
accuracy 0.19 70
macro avg 0.50 0.23 0.14 70
weighted avg 0.59 0.19 0.11 70
Training vgg with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 2.107501301500532
Epoch 2/10, Loss: 1.6367061535517375
Epoch 3/10, Loss: 1.663708766301473
Epoch 4/10, Loss: 1.6228733526335821
Epoch 5/10, Loss: 1.4933523138364155
Epoch 6/10, Loss: 1.3314513166745503
Epoch 7/10, Loss: 1.3220488296614752
Epoch 8/10, Loss: 1.2654757036103144
Epoch 9/10, Loss: 1.2509619659847684
Epoch 10/10, Loss: 1.6879863540331523
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.25 0.09 0.13 11
Mines 0.31 0.26 0.29 19
Pipes 0.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.23 0.92 0.37 12
accuracy 0.24 70
macro avg 0.36 0.25 0.16 70
weighted avg 0.32 0.24 0.16 70
Training vgg with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 3.1097815566592746
Epoch 2/10, Loss: 1.644615802499983
Epoch 3/10, Loss: 1.6246352791786194
Epoch 4/10, Loss: 1.6242347558339436
Epoch 5/10, Loss: 1.6306027505132887
Epoch 6/10, Loss: 1.6399134993553162
Epoch 7/10, Loss: 1.6291711860232883
Epoch 8/10, Loss: 1.5625028080410428
Epoch 9/10, Loss: 1.5822967158423529
Epoch 10/10, Loss: 1.5141244000858731
Accuracy: 25.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.38 0.27 0.32 11
Mines 0.31 0.53 0.39 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 0.42 0.24 12
accuracy 0.26 70
macro avg 0.57 0.24 0.19 70
weighted avg 0.57 0.26 0.20 70
Training vgg with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.7358186509874132
Epoch 2/10, Loss: 1.6373076770040724
Epoch 3/10, Loss: 1.6215023795763652
Epoch 4/10, Loss: 1.6320343017578125
Epoch 5/10, Loss: 1.6084040933185153
Epoch 6/10, Loss: 1.7518806788656447
Epoch 7/10, Loss: 1.5935512648688421
Epoch 8/10, Loss: 1.5587112175093756
Epoch 9/10, Loss: 1.6198596159617107
Epoch 10/10, Loss: 1.6045637395646837
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.8469773597187467
Epoch 2/10, Loss: 1.6601983507474263
Epoch 3/10, Loss: 1.6343557900852628
Epoch 4/10, Loss: 1.6343208220269945
Epoch 5/10, Loss: 1.6261424620946248
Epoch 6/10, Loss: 1.6103452179167006
Epoch 7/10, Loss: 1.6546049118041992
Epoch 8/10, Loss: 1.6692511836687725
Epoch 9/10, Loss: 1.6478998329904344
Epoch 10/10, Loss: 1.6401629315482245
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.819464749760098
Epoch 2/10, Loss: 1.6884954770406086
Epoch 3/10, Loss: 1.639943818251292
Epoch 4/10, Loss: 1.6277495291497972
Epoch 5/10, Loss: 1.645386086569892
Epoch 6/10, Loss: 1.6281471583578322
Epoch 7/10, Loss: 1.616923299100664
Epoch 8/10, Loss: 1.6203348239262898
Epoch 9/10, Loss: 1.7332429885864258
Epoch 10/10, Loss: 1.6719215446048312
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.16 1.00 0.27 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.16 70
macro avg 0.83 0.20 0.05 70
weighted avg 0.87 0.16 0.04 70
Training vgg with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 2.0101672808329263
Epoch 2/10, Loss: 1.703322410583496
Epoch 3/10, Loss: 1.6495218012068007
Epoch 4/10, Loss: 1.6403857337103949
Epoch 5/10, Loss: 1.6342239247428045
Epoch 6/10, Loss: 3.656427866882748
Epoch 7/10, Loss: 1.7032349507013957
Epoch 8/10, Loss: 1.679436789618598
Epoch 9/10, Loss: 1.6164943906995985
Epoch 10/10, Loss: 1.6276086237695482
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.5420486397213407
Epoch 2/10, Loss: 1.0191962685849931
Epoch 3/10, Loss: 0.6388836602369944
Epoch 4/10, Loss: 0.28572663804516196
Epoch 5/10, Loss: 0.1880058523060547
Epoch 6/10, Loss: 0.10892470243076484
Epoch 7/10, Loss: 0.06471467796816593
Epoch 8/10, Loss: 0.047585252064487174
Epoch 9/10, Loss: 0.04351052553910348
Epoch 10/10, Loss: 0.0346237915283483
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 1.00 0.79 0.88 19
Pipes 1.00 0.94 0.97 17
Rockets 0.90 0.82 0.86 11
Vehicles 0.79 0.92 0.85 12
accuracy 0.89 70
macro avg 0.88 0.89 0.88 70
weighted avg 0.91 0.89 0.89 70
Training vgg with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.5383455554644268
Epoch 2/10, Loss: 0.962391671207216
Epoch 3/10, Loss: 0.5865533335341347
Epoch 4/10, Loss: 0.3207651836176713
Epoch 5/10, Loss: 0.2008852134976122
Epoch 6/10, Loss: 0.1849039364606142
Epoch 7/10, Loss: 0.1260102785890922
Epoch 8/10, Loss: 0.20613905063105953
Epoch 9/10, Loss: 0.1464936742041674
Epoch 10/10, Loss: 0.06413883944818129
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 0.94 0.79 0.86 19
Pipes 1.00 0.94 0.97 17
Rockets 0.82 0.82 0.82 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.87 70
macro avg 0.87 0.88 0.87 70
weighted avg 0.89 0.87 0.87 70
Training vgg with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.610349178314209
Epoch 2/10, Loss: 1.183081954717636
Epoch 3/10, Loss: 0.8118613196743859
Epoch 4/10, Loss: 0.5304368497389886
Epoch 5/10, Loss: 0.19251448956007758
Epoch 6/10, Loss: 0.28060629901786643
Epoch 7/10, Loss: 0.3264051067332427
Epoch 8/10, Loss: 0.1311727713069154
Epoch 9/10, Loss: 0.07002848642878234
Epoch 10/10, Loss: 0.12900832627848205
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 1.00 0.84 0.91 19
Pipes 1.00 0.94 0.97 17
Rockets 0.75 0.82 0.78 11
Vehicles 0.85 0.92 0.88 12
accuracy 0.90 70
macro avg 0.89 0.90 0.89 70
weighted avg 0.91 0.90 0.90 70
Training vgg with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5462788542111714
Epoch 2/10, Loss: 0.945001870393753
Epoch 3/10, Loss: 0.5453693312075403
Epoch 4/10, Loss: 0.23126095057361656
Epoch 5/10, Loss: 0.10591271167827977
Epoch 6/10, Loss: 0.06982448690946007
Epoch 7/10, Loss: 0.0729612548279369
Epoch 8/10, Loss: 0.018886133064774588
Epoch 9/10, Loss: 0.006395092780520726
Epoch 10/10, Loss: 0.0025139508895032727
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.95 1.00 0.97 19
Pipes 1.00 0.94 0.97 17
Rockets 0.82 0.82 0.82 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.93 70
macro avg 0.92 0.92 0.92 70
weighted avg 0.93 0.93 0.93 70
Training vgg with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5578179160753887
Epoch 2/10, Loss: 0.98586548699273
Epoch 3/10, Loss: 0.5338143308957418
Epoch 4/10, Loss: 0.5082027812798818
Epoch 5/10, Loss: 0.40343746749891174
Epoch 6/10, Loss: 0.22865620131293932
Epoch 7/10, Loss: 0.18035926731924215
Epoch 8/10, Loss: 0.07559714324047996
Epoch 9/10, Loss: 0.01659870011240451
Epoch 10/10, Loss: 0.005176332756996594
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.93 0.74 0.82 19
Pipes 1.00 0.94 0.97 17
Rockets 0.79 1.00 0.88 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.89 70
macro avg 0.88 0.90 0.88 70
weighted avg 0.90 0.89 0.88 70
Training vgg with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5925413635041978
Epoch 2/10, Loss: 1.156813273827235
Epoch 3/10, Loss: 0.7109149032168918
Epoch 4/10, Loss: 0.5085729484756788
Epoch 5/10, Loss: 0.3588018498073022
Epoch 6/10, Loss: 0.22379358081767955
Epoch 7/10, Loss: 0.17477605388396317
Epoch 8/10, Loss: 0.1337748772671653
Epoch 9/10, Loss: 0.19229858948124778
Epoch 10/10, Loss: 0.21600088922099936
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.84 0.84 0.84 19
Pipes 1.00 0.88 0.94 17
Rockets 0.69 1.00 0.81 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.87 70
macro avg 0.89 0.88 0.87 70
weighted avg 0.89 0.87 0.87 70
Training vgg with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.518190986580319
Epoch 2/10, Loss: 0.9742649727397494
Epoch 3/10, Loss: 0.5353313833475113
Epoch 4/10, Loss: 0.4550214169753922
Epoch 5/10, Loss: 0.26607705652713776
Epoch 6/10, Loss: 0.13831671679185498
Epoch 7/10, Loss: 0.10047782859894344
Epoch 8/10, Loss: 0.26659279549494386
Epoch 9/10, Loss: 0.13237634792716968
Epoch 10/10, Loss: 0.023986874264664948
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.89 0.84 0.86 19
Pipes 0.94 0.94 0.94 17
Rockets 0.82 0.82 0.82 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.89 70
macro avg 0.88 0.89 0.88 70
weighted avg 0.89 0.89 0.89 70
Training vgg with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.494947658644782
Epoch 2/10, Loss: 0.9582508736186557
Epoch 3/10, Loss: 0.6275810582770242
Epoch 4/10, Loss: 0.36222601268026566
Epoch 5/10, Loss: 0.16991303716268805
Epoch 6/10, Loss: 0.37797444872558117
Epoch 7/10, Loss: 0.28162448811862206
Epoch 8/10, Loss: 0.13765869651817614
Epoch 9/10, Loss: 0.09182909519101183
Epoch 10/10, Loss: 0.05732820952673339
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.55 1.00 0.71 11
Mines 1.00 0.68 0.81 19
Pipes 0.94 0.94 0.94 17
Rockets 0.73 0.73 0.73 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.80 70
macro avg 0.82 0.80 0.79 70
weighted avg 0.85 0.80 0.81 70
Training vgg with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5721673766771953
Epoch 2/10, Loss: 1.034527666038937
Epoch 3/10, Loss: 0.7593460472093688
Epoch 4/10, Loss: 0.561187259025044
Epoch 5/10, Loss: 0.3354833357863956
Epoch 6/10, Loss: 0.2624076832499769
Epoch 7/10, Loss: 0.20472026699119145
Epoch 8/10, Loss: 0.060398021092017494
Epoch 9/10, Loss: 0.017004447400621656
Epoch 10/10, Loss: 0.019909052378756717
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.81 0.89 0.85 19
Pipes 1.00 0.76 0.87 17
Rockets 0.77 0.91 0.83 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.86 70
macro avg 0.87 0.86 0.86 70
weighted avg 0.88 0.86 0.86 70
Training vgg with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 112772913.26497255
Epoch 2/10, Loss: 38.648027804162766
Epoch 3/10, Loss: 31.384240402115715
Epoch 4/10, Loss: 5.882540192868975
Epoch 5/10, Loss: 2.141014880604214
Epoch 6/10, Loss: 1.6375006768438551
Epoch 7/10, Loss: 1.619797620508406
Epoch 8/10, Loss: 1.9725005229314168
Epoch 9/10, Loss: 1.5309086110856798
Epoch 10/10, Loss: 1.4121215807067022
Accuracy: 21.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.28 0.82 0.42 11
Mines 0.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.25 0.36 0.30 11
Vehicles 0.10 0.17 0.12 12
accuracy 0.21 70
macro avg 0.33 0.27 0.17 70
weighted avg 0.34 0.21 0.13 70
Training vgg with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 59865019.14749178
Epoch 2/10, Loss: 125.83830418189366
Epoch 3/10, Loss: 4.269839425881703
Epoch 4/10, Loss: 1.8058466580179002
Epoch 5/10, Loss: 1.6365250680181715
Epoch 6/10, Loss: 1.626505070262485
Epoch 7/10, Loss: 1.609434809949663
Epoch 8/10, Loss: 1.628051499525706
Epoch 9/10, Loss: 1.6101391580369737
Epoch 10/10, Loss: 1.6117907166481018
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 396173917.76011753
Epoch 2/10, Loss: 16.87352752685547
Epoch 3/10, Loss: 1.6746929751502142
Epoch 4/10, Loss: 1.62697039047877
Epoch 5/10, Loss: 1.6499869293636746
Epoch 6/10, Loss: 2.059490382671356
Epoch 7/10, Loss: 1.6224370797475178
Epoch 8/10, Loss: 1.5744594666692946
Epoch 9/10, Loss: 1.5783622728453741
Epoch 10/10, Loss: 1.5380827850765653
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.22 1.00 0.36 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.25 0.45 0.32 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.23 70
macro avg 0.69 0.29 0.14 70
weighted avg 0.76 0.23 0.11 70
Training vgg with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 2948383934.175385
Epoch 2/10, Loss: 17.16642885737949
Epoch 3/10, Loss: 4.218594663672977
Epoch 4/10, Loss: 1.6160539388656616
Epoch 5/10, Loss: 1.62196546792984
Epoch 6/10, Loss: 1.5876815451516046
Epoch 7/10, Loss: 1.5885229309399922
Epoch 8/10, Loss: 1.836177991496192
Epoch 9/10, Loss: 19.218657460477615
Epoch 10/10, Loss: 2.558550854523977
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 251516417.28112254
Epoch 2/10, Loss: 28.759647323025597
Epoch 3/10, Loss: 1.6289940410190158
Epoch 4/10, Loss: 3.375627272658878
Epoch 5/10, Loss: 5.044012169043223
Epoch 6/10, Loss: 1.8780815137757196
Epoch 7/10, Loss: 5.193899359967974
Epoch 8/10, Loss: 1.6940929757224188
Epoch 9/10, Loss: 9.366021394729614
Epoch 10/10, Loss: 2.3296050826708474
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.15 0.55 0.24 11
Mines 1.00 0.00 0.00 19
Pipes 0.37 0.59 0.45 17
Rockets 0.33 0.09 0.14 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.24 70
macro avg 0.57 0.24 0.17 70
weighted avg 0.61 0.24 0.17 70
Training vgg with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 176721781.83417854
Epoch 2/10, Loss: 1.6437566147910223
Epoch 3/10, Loss: 40.66675774256388
Epoch 4/10, Loss: 28.523877614074284
Epoch 5/10, Loss: 142.92668869098028
Epoch 6/10, Loss: 1.8552033636305068
Epoch 7/10, Loss: 2.838498844040765
Epoch 8/10, Loss: 2.410163680712382
Epoch 9/10, Loss: 1.7806028326352437
Epoch 10/10, Loss: 1.6122309896681044
Accuracy: 21.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 0.00 0.00 0.00 17
Rockets 0.17 1.00 0.30 11
Vehicles 0.67 0.33 0.44 12
accuracy 0.21 70
macro avg 0.57 0.27 0.15 70
weighted avg 0.57 0.21 0.12 70
Training vgg with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1433164995.340821
Epoch 2/10, Loss: 347.0227549407217
Epoch 3/10, Loss: 11.370750996801588
Epoch 4/10, Loss: 3.660609847969479
Epoch 5/10, Loss: 2.2504809300104776
Epoch 6/10, Loss: 1.6894724435276456
Epoch 7/10, Loss: 1.6382007400194805
Epoch 8/10, Loss: 1.7281353804800246
Epoch 9/10, Loss: 5.648725165261163
Epoch 10/10, Loss: 4.282868597242567
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.16 1.00 0.27 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.16 70
macro avg 0.83 0.20 0.05 70
weighted avg 0.87 0.16 0.04 70
Training vgg with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 127566520.55737884
Epoch 2/10, Loss: 184.51930018928317
Epoch 3/10, Loss: 1.6910654703776042
Epoch 4/10, Loss: 1.6042450070381165
Epoch 5/10, Loss: 23.276108152336544
Epoch 6/10, Loss: 5.294715311792162
Epoch 7/10, Loss: 1.6184197068214417
Epoch 8/10, Loss: 1.6680784026781719
Epoch 9/10, Loss: 2.169477058781518
Epoch 10/10, Loss: 1.7424066132969327
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.27 1.00 0.43 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.27 70
macro avg 0.85 0.20 0.09 70
weighted avg 0.80 0.27 0.12 70
Training vgg with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 572917296.498081
Epoch 2/10, Loss: 74.78298558791478
Epoch 3/10, Loss: 2.4130763080385
Epoch 4/10, Loss: 2.1930867632230124
Epoch 5/10, Loss: 1.9617750710911221
Epoch 6/10, Loss: 1.7504637175136142
Epoch 7/10, Loss: 4.2158649696244135
Epoch 8/10, Loss: 1.6211201416121588
Epoch 9/10, Loss: 1.607596430513594
Epoch 10/10, Loss: 2.8470770120620728
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.06 0.09 0.07 11
Vehicles 0.19 0.83 0.30 12
accuracy 0.16 70
macro avg 0.65 0.18 0.08 70
weighted avg 0.71 0.16 0.06 70
Training vgg with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.9516211218304105
Epoch 2/10, Loss: 1.716561132007175
Epoch 3/10, Loss: 1.599764969613817
Epoch 4/10, Loss: 1.6217046711179945
Epoch 5/10, Loss: 1.6111458539962769
Epoch 6/10, Loss: 1.6447964509328206
Epoch 7/10, Loss: 2.588021198908488
Epoch 8/10, Loss: 1.6531297498279147
Epoch 9/10, Loss: 1.6082489490509033
Epoch 10/10, Loss: 1.578813248210483
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.45 0.56 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.18 0.91 0.29 11
Vehicles 0.33 0.17 0.22 12
accuracy 0.24 70
macro avg 0.64 0.31 0.21 70
weighted avg 0.71 0.24 0.17 70
Training vgg with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.8938304980595906
Epoch 2/10, Loss: 1.6364357471466064
Epoch 3/10, Loss: 1.6280709637535944
Epoch 4/10, Loss: 1.6204626825120714
Epoch 5/10, Loss: 1.6417034599516127
Epoch 6/10, Loss: 1.624459120962355
Epoch 7/10, Loss: 1.6249762508604262
Epoch 8/10, Loss: 1.6240202850765653
Epoch 9/10, Loss: 1.6116328371895685
Epoch 10/10, Loss: 1.6247202025519476
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.9465992450714111
Epoch 2/10, Loss: 1.623832901318868
Epoch 3/10, Loss: 1.6138816012276544
Epoch 4/10, Loss: 1.6338964568244085
Epoch 5/10, Loss: 1.6123270326190524
Epoch 6/10, Loss: 1.6163157886928983
Epoch 7/10, Loss: 1.5391311513053045
Epoch 8/10, Loss: 1.6232467359966702
Epoch 9/10, Loss: 1.5840257273779974
Epoch 10/10, Loss: 1.576628115442064
Accuracy: 18.57%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.16 0.82 0.27 11
Vehicles 0.29 0.33 0.31 12
accuracy 0.19 70
macro avg 0.69 0.23 0.12 70
weighted avg 0.75 0.19 0.09 70
Training vgg with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 2.1300858656565347
Epoch 2/10, Loss: 1.624049425125122
Epoch 3/10, Loss: 1.6071522103415594
Epoch 4/10, Loss: 1.6657174825668335
Epoch 5/10, Loss: 1.6151347292794123
Epoch 6/10, Loss: 1.6152029302385118
Epoch 7/10, Loss: 2.139059729046292
Epoch 8/10, Loss: 1.6185625129275851
Epoch 9/10, Loss: 1.60853025648329
Epoch 10/10, Loss: 1.6209551095962524
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.9055177370707195
Epoch 2/10, Loss: 1.6551762951744928
Epoch 3/10, Loss: 1.6242674589157104
Epoch 4/10, Loss: 1.6177988317277696
Epoch 5/10, Loss: 1.6277790069580078
Epoch 6/10, Loss: 1.6196397542953491
Epoch 7/10, Loss: 1.5427804390589397
Epoch 8/10, Loss: 1.4848989910549588
Epoch 9/10, Loss: 1.4242835177315607
Epoch 10/10, Loss: 1.3779576884375677
Accuracy: 21.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.40 0.36 0.38 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.22 0.64 0.33 11
Vehicles 0.14 0.33 0.20 12
accuracy 0.21 70
macro avg 0.55 0.27 0.18 70
weighted avg 0.64 0.21 0.15 70
Training vgg with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.8600751823849149
Epoch 2/10, Loss: 1.6166761451297336
Epoch 3/10, Loss: 1.6284231079949274
Epoch 4/10, Loss: 1.6842495203018188
Epoch 5/10, Loss: 1.6283522182040744
Epoch 6/10, Loss: 1.6182933383517795
Epoch 7/10, Loss: 1.6320600377188788
Epoch 8/10, Loss: 1.6185231738620334
Epoch 9/10, Loss: 1.6351003779305353
Epoch 10/10, Loss: 1.6364766889148288
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 3.0835534731547036
Epoch 2/10, Loss: 1.736477745903863
Epoch 3/10, Loss: 2.344721515973409
Epoch 4/10, Loss: 1.6201619837019179
Epoch 5/10, Loss: 1.6361427572038438
Epoch 6/10, Loss: 1.7426771455340915
Epoch 7/10, Loss: 1.6091065804163616
Epoch 8/10, Loss: 1.6149460739559598
Epoch 9/10, Loss: 1.6241846217049494
Epoch 10/10, Loss: 1.6097352769639757
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 2.0513836410310535
Epoch 2/10, Loss: 1.6205881039301555
Epoch 3/10, Loss: 1.6306802961561415
Epoch 4/10, Loss: 1.619098424911499
Epoch 5/10, Loss: 1.6364724106258817
Epoch 6/10, Loss: 1.6152932246526082
Epoch 7/10, Loss: 1.6219936476813421
Epoch 8/10, Loss: 1.6012435489230685
Epoch 9/10, Loss: 1.5985326634513006
Epoch 10/10, Loss: 1.5231237279044256
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.43 0.27 0.33 11
Mines 1.00 0.05 0.10 19
Pipes 1.00 0.00 0.00 17
Rockets 0.14 0.36 0.21 11
Vehicles 0.12 0.33 0.17 12
accuracy 0.17 70
macro avg 0.54 0.20 0.16 70
weighted avg 0.62 0.17 0.14 70
Training vgg with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 2.086209429634942
Epoch 2/10, Loss: 1.6218168338139851
Epoch 3/10, Loss: 1.614229268497891
Epoch 4/10, Loss: 1.8552527295218573
Epoch 5/10, Loss: 1.6543287568622165
Epoch 6/10, Loss: 1.650309059354994
Epoch 7/10, Loss: 1.6098670429653592
Epoch 8/10, Loss: 1.5788803233040705
Epoch 9/10, Loss: 1.7186606460147433
Epoch 10/10, Loss: 1.6391155852211847
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.5800405343373616
Epoch 2/10, Loss: 1.2515542440944247
Epoch 3/10, Loss: 0.8643905917803446
Epoch 4/10, Loss: 0.46911502215597367
Epoch 5/10, Loss: 0.2981729209423065
Epoch 6/10, Loss: 0.17938772257831362
Epoch 7/10, Loss: 0.13959033373329374
Epoch 8/10, Loss: 0.09774585637367433
Epoch 9/10, Loss: 0.06791780537201299
Epoch 10/10, Loss: 0.022167657838306494
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.73 0.84 11
Mines 0.90 1.00 0.95 19
Pipes 0.80 0.94 0.86 17
Rockets 0.80 0.73 0.76 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.87 70
macro avg 0.88 0.85 0.86 70
weighted avg 0.88 0.87 0.87 70
Training vgg with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6480451160007052
Epoch 2/10, Loss: 1.3604648510615032
Epoch 3/10, Loss: 1.0152775446573894
Epoch 4/10, Loss: 0.6813206606441073
Epoch 5/10, Loss: 0.5158800284067789
Epoch 6/10, Loss: 0.258129992418819
Epoch 7/10, Loss: 0.2281548703710238
Epoch 8/10, Loss: 0.23918746991289985
Epoch 9/10, Loss: 0.126658762494723
Epoch 10/10, Loss: 0.06985160397986571
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.94 0.84 0.89 19
Pipes 1.00 0.94 0.97 17
Rockets 0.67 0.91 0.77 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.87 70
macro avg 0.88 0.87 0.86 70
weighted avg 0.90 0.87 0.87 70
Training vgg with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.70245541466607
Epoch 2/10, Loss: 1.3798496590720282
Epoch 3/10, Loss: 1.1336644689242046
Epoch 4/10, Loss: 0.8574367165565491
Epoch 5/10, Loss: 0.5852057801352607
Epoch 6/10, Loss: 0.35928764939308167
Epoch 7/10, Loss: 0.18903357038895288
Epoch 8/10, Loss: 0.09703298445997967
Epoch 9/10, Loss: 0.11219204175803396
Epoch 10/10, Loss: 0.06377674132171604
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.90 1.00 0.95 19
Pipes 1.00 0.88 0.94 17
Rockets 0.83 0.91 0.87 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.93 70
macro avg 0.93 0.92 0.92 70
weighted avg 0.93 0.93 0.93 70
Training vgg with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6066012779871623
Epoch 2/10, Loss: 1.3242222600513034
Epoch 3/10, Loss: 0.9458409282896254
Epoch 4/10, Loss: 0.6371308664480845
Epoch 5/10, Loss: 0.38238564299212563
Epoch 6/10, Loss: 0.28714001841015285
Epoch 7/10, Loss: 0.14560021460056305
Epoch 8/10, Loss: 0.1422792530308167
Epoch 9/10, Loss: 0.06132605920235316
Epoch 10/10, Loss: 0.039005950196749635
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.94 0.89 0.92 19
Pipes 1.00 0.94 0.97 17
Rockets 0.83 0.91 0.87 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.91 70
macro avg 0.91 0.92 0.91 70
weighted avg 0.92 0.91 0.91 70
Training vgg with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6575291156768799
Epoch 2/10, Loss: 1.3895608186721802
Epoch 3/10, Loss: 1.098004506693946
Epoch 4/10, Loss: 0.7225776380962796
Epoch 5/10, Loss: 0.47487234075864154
Epoch 6/10, Loss: 0.33943548964129555
Epoch 7/10, Loss: 0.15549344155523512
Epoch 8/10, Loss: 0.09682880673143598
Epoch 9/10, Loss: 0.05385660297340817
Epoch 10/10, Loss: 0.020100300717684958
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.81 0.89 0.85 19
Pipes 1.00 0.88 0.94 17
Rockets 0.91 0.91 0.91 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.89 70
macro avg 0.90 0.89 0.89 70
weighted avg 0.90 0.89 0.89 70
Training vgg with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6539653937021892
Epoch 2/10, Loss: 1.4170070754157171
Epoch 3/10, Loss: 1.1365121470557318
Epoch 4/10, Loss: 0.7966760264502631
Epoch 5/10, Loss: 0.516997797621621
Epoch 6/10, Loss: 0.33728451530138653
Epoch 7/10, Loss: 0.2292557524310218
Epoch 8/10, Loss: 0.13074887585308817
Epoch 9/10, Loss: 0.10293520914597644
Epoch 10/10, Loss: 0.05474393292226725
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.89 0.84 0.86 19
Pipes 1.00 0.88 0.94 17
Rockets 0.83 0.91 0.87 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.89 70
macro avg 0.88 0.89 0.88 70
weighted avg 0.89 0.89 0.89 70
Training vgg with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6089728275934856
Epoch 2/10, Loss: 1.3032869630389743
Epoch 3/10, Loss: 0.9204140106836954
Epoch 4/10, Loss: 0.5707922677199045
Epoch 5/10, Loss: 0.37187536391947007
Epoch 6/10, Loss: 0.22119649582439
Epoch 7/10, Loss: 0.11273412365052435
Epoch 8/10, Loss: 0.05052007558859057
Epoch 9/10, Loss: 0.019372130433718365
Epoch 10/10, Loss: 0.011098627518448565
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.85 0.89 0.87 19
Pipes 1.00 0.88 0.94 17
Rockets 0.83 0.91 0.87 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.89 70
macro avg 0.89 0.89 0.88 70
weighted avg 0.90 0.89 0.89 70
Training vgg with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.629978444841173
Epoch 2/10, Loss: 1.362365232573615
Epoch 3/10, Loss: 1.0058811240726047
Epoch 4/10, Loss: 0.6234390073352389
Epoch 5/10, Loss: 0.43896473778618705
Epoch 6/10, Loss: 0.26279378102885353
Epoch 7/10, Loss: 0.16900719536675346
Epoch 8/10, Loss: 0.08065774788459142
Epoch 9/10, Loss: 0.10492752513123883
Epoch 10/10, Loss: 0.06237004448970159
Accuracy: 74.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.55 1.00 0.71 11
Mines 0.71 0.79 0.75 19
Pipes 1.00 0.76 0.87 17
Rockets 0.67 0.55 0.60 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.74 70
macro avg 0.79 0.74 0.73 70
weighted avg 0.80 0.74 0.75 70
Training vgg with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.691113723648919
Epoch 2/10, Loss: 1.4282159805297852
Epoch 3/10, Loss: 1.1011391348308988
Epoch 4/10, Loss: 0.7934585644139184
Epoch 5/10, Loss: 0.5673246681690216
Epoch 6/10, Loss: 0.39859162436591256
Epoch 7/10, Loss: 0.30311326185862225
Epoch 8/10, Loss: 0.165684022837215
Epoch 9/10, Loss: 0.10484412995477517
Epoch 10/10, Loss: 0.09890622790488932
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.81 0.89 0.85 19
Pipes 1.00 0.88 0.94 17
Rockets 0.83 0.91 0.87 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.89 70
macro avg 0.90 0.89 0.89 70
weighted avg 0.90 0.89 0.89 70
Training vgg with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1675833412.513906
Epoch 2/10, Loss: 311.2754477394952
Epoch 3/10, Loss: 116.74438026216295
Epoch 4/10, Loss: 29.339422782262165
Epoch 5/10, Loss: 2.343207663959927
Epoch 6/10, Loss: 1.6140879525078669
Epoch 7/10, Loss: 1.8894090917375352
Epoch 8/10, Loss: 1.6575855546527438
Epoch 9/10, Loss: 1.5178677903281317
Epoch 10/10, Loss: 1.4066536161634657
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.33 0.27 0.30 11
Mines 0.34 0.89 0.49 19
Pipes 0.00 0.00 0.00 17
Rockets 0.33 0.18 0.24 11
Vehicles 1.00 0.33 0.50 12
accuracy 0.37 70
macro avg 0.40 0.34 0.31 70
weighted avg 0.37 0.37 0.30 70
Training vgg with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 476256142.3264225
Epoch 2/10, Loss: 1558.3666008843315
Epoch 3/10, Loss: 173.95957758691577
Epoch 4/10, Loss: 4.66334515147739
Epoch 5/10, Loss: 6.8280814621183605
Epoch 6/10, Loss: 1.8832475476794772
Epoch 7/10, Loss: 1.9783724281522963
Epoch 8/10, Loss: 1.7531710465749104
Epoch 9/10, Loss: 1.6367564996083577
Epoch 10/10, Loss: 2.123158719804552
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 0.24 1.00 0.39 17
Rockets 1.00 0.00 0.00 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.24 70
macro avg 0.85 0.20 0.08 70
weighted avg 0.82 0.24 0.09 70
Training vgg with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 414979420.25028044
Epoch 2/10, Loss: 5323.69657643636
Epoch 3/10, Loss: 42.02121186256409
Epoch 4/10, Loss: 24.065033290121292
Epoch 5/10, Loss: 2.1027694940567017
Epoch 6/10, Loss: 11.521882110171848
Epoch 7/10, Loss: 1.7079717715581257
Epoch 8/10, Loss: 1.8413636816872492
Epoch 9/10, Loss: 1.6858333746592205
Epoch 10/10, Loss: 1.5717577934265137
Accuracy: 31.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.08 0.09 0.08 11
Mines 1.00 0.00 0.00 19
Pipes 0.35 0.71 0.47 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.39 0.75 0.51 12
accuracy 0.31 70
macro avg 0.56 0.31 0.21 70
weighted avg 0.59 0.31 0.22 70
Training vgg with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 613327351.2087208
Epoch 2/10, Loss: 491.9457492828369
Epoch 3/10, Loss: 17.24325007862515
Epoch 4/10, Loss: 11.915071262253655
Epoch 5/10, Loss: 1.8579604625701904
Epoch 6/10, Loss: 1.6041547722286649
Epoch 7/10, Loss: 36.4389918645223
Epoch 8/10, Loss: 23.140766435199314
Epoch 9/10, Loss: 1.6295763651529949
Epoch 10/10, Loss: 1.6226601600646973
Accuracy: 14.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.10 0.09 0.10 11
Mines 0.21 0.16 0.18 19
Pipes 1.00 0.00 0.00 17
Rockets 0.10 0.27 0.14 11
Vehicles 0.20 0.25 0.22 12
accuracy 0.14 70
macro avg 0.32 0.15 0.13 70
weighted avg 0.37 0.14 0.12 70
Training vgg with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 177275743.57260752
Epoch 2/10, Loss: 22420.908807330663
Epoch 3/10, Loss: 405.1314705212911
Epoch 4/10, Loss: 40.99927677048577
Epoch 5/10, Loss: 1.9607847796546087
Epoch 6/10, Loss: 1.612760755750868
Epoch 7/10, Loss: 1.6179001463784113
Epoch 8/10, Loss: 1.6214449670579698
Epoch 9/10, Loss: 1.611070606443617
Epoch 10/10, Loss: 1.6119833522372775
Accuracy: 21.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.28 0.42 0.33 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 0.58 0.26 12
accuracy 0.21 70
macro avg 0.69 0.20 0.12 70
weighted avg 0.66 0.21 0.14 70
Training vgg with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 148756564.41394436
Epoch 2/10, Loss: 650.0253381994036
Epoch 3/10, Loss: 1.6386367877324421
Epoch 4/10, Loss: 51.81411499447293
Epoch 5/10, Loss: 1.6235175397660997
Epoch 6/10, Loss: 1.6116562287012737
Epoch 7/10, Loss: 742.9962830146154
Epoch 8/10, Loss: 11160.496765587064
Epoch 9/10, Loss: 15.105101267496744
Epoch 10/10, Loss: 1.6130130026075575
Accuracy: 14.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.14 0.18 0.16 11
Vehicles 0.14 0.67 0.24 12
accuracy 0.14 70
macro avg 0.66 0.17 0.08 70
weighted avg 0.72 0.14 0.07 70
Training vgg with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1262760274.3107264
Epoch 2/10, Loss: 534.3947941727108
Epoch 3/10, Loss: 235.77276961008707
Epoch 4/10, Loss: 5.134722179836697
Epoch 5/10, Loss: 28.066193249490524
Epoch 6/10, Loss: 2.1895337634616427
Epoch 7/10, Loss: 1.619184997346666
Epoch 8/10, Loss: 1.6361914740668402
Epoch 9/10, Loss: 1.5995185905032687
Epoch 10/10, Loss: 1.6603772640228271
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1728735362.2522702
Epoch 2/10, Loss: 1478.5515916082595
Epoch 3/10, Loss: 18.981079896291096
Epoch 4/10, Loss: 15.876200411054823
Epoch 5/10, Loss: 3.6601257589128284
Epoch 6/10, Loss: 1.7893999814987183
Epoch 7/10, Loss: 1.593048373858134
Epoch 8/10, Loss: 1.5967369079589844
Epoch 9/10, Loss: 1.885666913456387
Epoch 10/10, Loss: 1.618827184041341
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.19 0.64 0.30 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.29 0.83 0.43 12
accuracy 0.24 70
macro avg 0.70 0.29 0.15 70
weighted avg 0.75 0.24 0.12 70
Training vgg with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 80734515.59519053
Epoch 2/10, Loss: 861.1775483820173
Epoch 3/10, Loss: 1068.1982082790798
Epoch 4/10, Loss: 1026.9964611265395
Epoch 5/10, Loss: 27.405624389648438
Epoch 6/10, Loss: 1.9871438609229193
Epoch 7/10, Loss: 1.6223190360599093
Epoch 8/10, Loss: 1.6824404398600261
Epoch 9/10, Loss: 1.731573263804118
Epoch 10/10, Loss: 1.6033244132995605
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.09 0.17 11
Mines 0.33 0.11 0.16 19
Pipes 0.50 0.12 0.19 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.20 1.00 0.34 12
accuracy 0.24 70
macro avg 0.61 0.26 0.17 70
weighted avg 0.56 0.24 0.17 70
Training vgg with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 2.022183084487915
Epoch 2/10, Loss: 1.7210125923156738
Epoch 3/10, Loss: 1.6271447896957398
Epoch 4/10, Loss: 1.6119561672210694
Epoch 5/10, Loss: 1.6227951288223266
Epoch 6/10, Loss: 1.618059468269348
Epoch 7/10, Loss: 1.605904483795166
Epoch 8/10, Loss: 1.6234333992004395
Epoch 9/10, Loss: 1.5993881225585938
Epoch 10/10, Loss: 1.6061280727386475
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 2.1413461208343505
Epoch 2/10, Loss: 1.7475250482559204
Epoch 3/10, Loss: 1.6602445602416993
Epoch 4/10, Loss: 1.6477223634719849
Epoch 5/10, Loss: 1.6113701343536377
Epoch 6/10, Loss: 2.2615532159805296
Epoch 7/10, Loss: 1.6013684034347535
Epoch 8/10, Loss: 1.637786602973938
Epoch 9/10, Loss: 1.6123082160949707
Epoch 10/10, Loss: 1.5763890981674193
Accuracy: 28.57%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.30 0.84 0.44 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.24 0.33 0.28 12
accuracy 0.29 70
macro avg 0.71 0.24 0.14 70
weighted avg 0.68 0.29 0.17 70
Training vgg with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.8500224828720093
Epoch 2/10, Loss: 1.6438222408294678
Epoch 3/10, Loss: 1.8303279638290406
Epoch 4/10, Loss: 1.6132270336151122
Epoch 5/10, Loss: 1.599495029449463
Epoch 6/10, Loss: 1.6276414155960084
Epoch 7/10, Loss: 1.6075918674468994
Epoch 8/10, Loss: 1.6081703424453735
Epoch 9/10, Loss: 1.6141510963439942
Epoch 10/10, Loss: 1.616339349746704
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 3.438053560256958
Epoch 2/10, Loss: 1.6907749414443969
Epoch 3/10, Loss: 1.7411352157592774
Epoch 4/10, Loss: 1.6461132764816284
Epoch 5/10, Loss: 1.6187945365905763
Epoch 6/10, Loss: 1.6190475463867187
Epoch 7/10, Loss: 1.6017991542816161
Epoch 8/10, Loss: 1.5923151969909668
Epoch 9/10, Loss: 1.6141435384750367
Epoch 10/10, Loss: 1.6146963119506836
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 2.2079581022262573
Epoch 2/10, Loss: 1.7606695413589477
Epoch 3/10, Loss: 1.7320111751556397
Epoch 4/10, Loss: 1.6407416343688965
Epoch 5/10, Loss: 1.6055216312408447
Epoch 6/10, Loss: 1.6051282167434693
Epoch 7/10, Loss: 1.5820413827896118
Epoch 8/10, Loss: 1.5880171537399292
Epoch 9/10, Loss: 1.537420916557312
Epoch 10/10, Loss: 1.546416664123535
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.38 0.27 0.32 11
Mines 0.35 0.42 0.38 19
Pipes 1.00 0.00 0.00 17
Rockets 0.21 0.73 0.32 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.27 70
macro avg 0.59 0.28 0.20 70
weighted avg 0.60 0.27 0.20 70
Training vgg with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 2.365161395072937
Epoch 2/10, Loss: 1.8069922924041748
Epoch 3/10, Loss: 1.6394068717956543
Epoch 4/10, Loss: 1.6227983951568603
Epoch 5/10, Loss: 1.7517239809036256
Epoch 6/10, Loss: 1.63411705493927
Epoch 7/10, Loss: 1.604179358482361
Epoch 8/10, Loss: 1.6056365013122558
Epoch 9/10, Loss: 1.5954684734344482
Epoch 10/10, Loss: 1.5570106267929078
Accuracy: 25.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.25 0.73 0.37 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.16 0.27 0.20 11
Vehicles 0.37 0.58 0.45 12
accuracy 0.26 70
macro avg 0.56 0.32 0.20 70
weighted avg 0.64 0.26 0.17 70
Training vgg with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 3.143891453742981
Epoch 2/10, Loss: 1.6765167951583861
Epoch 3/10, Loss: 1.63231041431427
Epoch 4/10, Loss: 1.6112757444381713
Epoch 5/10, Loss: 1.6481729030609131
Epoch 6/10, Loss: 1.6442161083221436
Epoch 7/10, Loss: 1.7954745054244996
Epoch 8/10, Loss: 1.5946456909179687
Epoch 9/10, Loss: 1.532713031768799
Epoch 10/10, Loss: 1.5417001247406006
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.28 1.00 0.43 19
Pipes 1.00 0.00 0.00 17
Rockets 0.00 0.00 0.00 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.27 70
macro avg 0.66 0.20 0.09 70
weighted avg 0.65 0.27 0.12 70
Training vgg with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.9202302694320679
Epoch 2/10, Loss: 1.6407132625579834
Epoch 3/10, Loss: 1.7061997413635255
Epoch 4/10, Loss: 1.7127633333206176
Epoch 5/10, Loss: 1.6176158666610718
Epoch 6/10, Loss: 1.6103699922561645
Epoch 7/10, Loss: 1.5615001440048217
Epoch 8/10, Loss: 1.6623789072036743
Epoch 9/10, Loss: 1.661804485321045
Epoch 10/10, Loss: 1.5529871702194213
Accuracy: 18.57%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.16 1.00 0.28 11
Vehicles 1.00 0.17 0.29 12
accuracy 0.19 70
macro avg 0.83 0.23 0.11 70
weighted avg 0.87 0.19 0.09 70
Training vgg with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.9402653694152832
Epoch 2/10, Loss: 2.0619171380996706
Epoch 3/10, Loss: 1.653336191177368
Epoch 4/10, Loss: 1.6089587926864624
Epoch 5/10, Loss: 1.5999149084091187
Epoch 6/10, Loss: 1.6370497703552247
Epoch 7/10, Loss: 1.608517098426819
Epoch 8/10, Loss: 1.6403773546218872
Epoch 9/10, Loss: 1.5938467264175415
Epoch 10/10, Loss: 1.5570740222930908
Accuracy: 32.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.25 0.36 0.30 11
Mines 1.00 0.00 0.00 19
Pipes 0.40 0.47 0.43 17
Rockets 0.21 0.36 0.27 11
Vehicles 0.47 0.58 0.52 12
accuracy 0.33 70
macro avg 0.47 0.36 0.30 70
weighted avg 0.52 0.33 0.28 70
Training vgg with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6494877815246582
Epoch 2/10, Loss: 1.4760016202926636
Epoch 3/10, Loss: 1.31560537815094
Epoch 4/10, Loss: 1.1415388107299804
Epoch 5/10, Loss: 0.9068920016288757
Epoch 6/10, Loss: 0.6865150332450867
Epoch 7/10, Loss: 0.5514459788799286
Epoch 8/10, Loss: 0.4952600717544556
Epoch 9/10, Loss: 0.37755303978919985
Epoch 10/10, Loss: 0.30046480894088745
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 0.75 0.79 0.77 19
Pipes 1.00 0.71 0.83 17
Rockets 0.90 0.82 0.86 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.81 70
macro avg 0.83 0.83 0.82 70
weighted avg 0.84 0.81 0.82 70
Training vgg with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6680045127868652
Epoch 2/10, Loss: 1.5538413763046264
Epoch 3/10, Loss: 1.351307487487793
Epoch 4/10, Loss: 1.2033119916915893
Epoch 5/10, Loss: 1.0442859292030335
Epoch 6/10, Loss: 0.7967292308807373
Epoch 7/10, Loss: 0.5840274333953858
Epoch 8/10, Loss: 0.4831213176250458
Epoch 9/10, Loss: 0.3621987998485565
Epoch 10/10, Loss: 0.2710182875394821
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.94 0.84 0.89 19
Pipes 1.00 0.94 0.97 17
Rockets 0.77 0.91 0.83 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.89 70
macro avg 0.88 0.89 0.88 70
weighted avg 0.90 0.89 0.89 70
Training vgg with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.7254230737686158
Epoch 2/10, Loss: 1.5665702104568482
Epoch 3/10, Loss: 1.387677836418152
Epoch 4/10, Loss: 1.2665402173995972
Epoch 5/10, Loss: 1.0677042961120606
Epoch 6/10, Loss: 0.8323475241661071
Epoch 7/10, Loss: 0.6780678629875183
Epoch 8/10, Loss: 0.48642364144325256
Epoch 9/10, Loss: 0.3985962927341461
Epoch 10/10, Loss: 0.26890908777713773
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.81 0.89 0.85 19
Pipes 0.94 0.94 0.94 17
Rockets 0.70 0.64 0.67 11
Vehicles 0.69 0.75 0.72 12
accuracy 0.83 70
macro avg 0.83 0.81 0.82 70
weighted avg 0.83 0.83 0.83 70
Training vgg with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.7100231647491455
Epoch 2/10, Loss: 1.5349456071853638
Epoch 3/10, Loss: 1.3262632131576537
Epoch 4/10, Loss: 1.1269312500953674
Epoch 5/10, Loss: 0.909336256980896
Epoch 6/10, Loss: 0.7070351600646972
Epoch 7/10, Loss: 0.5506410121917724
Epoch 8/10, Loss: 0.4140510320663452
Epoch 9/10, Loss: 0.2839623689651489
Epoch 10/10, Loss: 0.19984073042869568
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 0.88 0.79 0.83 19
Pipes 1.00 0.88 0.94 17
Rockets 0.82 0.82 0.82 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.86 70
macro avg 0.86 0.86 0.85 70
weighted avg 0.87 0.86 0.86 70
Training vgg with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7395294666290284
Epoch 2/10, Loss: 1.5314008235931396
Epoch 3/10, Loss: 1.3416820526123048
Epoch 4/10, Loss: 1.1891619920730592
Epoch 5/10, Loss: 1.0128508448600768
Epoch 6/10, Loss: 0.7286567091941833
Epoch 7/10, Loss: 0.5560286819934845
Epoch 8/10, Loss: 0.439534866809845
Epoch 9/10, Loss: 0.3064605385065079
Epoch 10/10, Loss: 0.27790352404117585
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.61 1.00 0.76 11
Mines 0.89 0.84 0.86 19
Pipes 1.00 0.88 0.94 17
Rockets 0.82 0.82 0.82 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.84 70
macro avg 0.86 0.84 0.84 70
weighted avg 0.88 0.84 0.85 70
Training vgg with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6928292989730835
Epoch 2/10, Loss: 1.5640751600265503
Epoch 3/10, Loss: 1.4061031341552734
Epoch 4/10, Loss: 1.2548629760742187
Epoch 5/10, Loss: 1.060383427143097
Epoch 6/10, Loss: 0.8320559740066529
Epoch 7/10, Loss: 0.670966637134552
Epoch 8/10, Loss: 0.481805020570755
Epoch 9/10, Loss: 0.3625888377428055
Epoch 10/10, Loss: 0.3288699597120285
Accuracy: 78.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.87 0.68 0.76 19
Pipes 1.00 0.94 0.97 17
Rockets 0.58 0.64 0.61 11
Vehicles 0.64 0.75 0.69 12
accuracy 0.79 70
macro avg 0.77 0.78 0.77 70
weighted avg 0.80 0.79 0.79 70
Training vgg with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6875673055648803
Epoch 2/10, Loss: 1.5374699831008911
Epoch 3/10, Loss: 1.3642640829086303
Epoch 4/10, Loss: 1.1976745128631592
Epoch 5/10, Loss: 0.9986406445503235
Epoch 6/10, Loss: 0.7873573660850525
Epoch 7/10, Loss: 0.5759502291679383
Epoch 8/10, Loss: 0.4348676264286041
Epoch 9/10, Loss: 0.36619722843170166
Epoch 10/10, Loss: 0.2608090490102768
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.55 0.71 11
Mines 0.63 1.00 0.78 19
Pipes 1.00 0.82 0.90 17
Rockets 0.78 0.64 0.70 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.80 70
macro avg 0.86 0.77 0.79 70
weighted avg 0.85 0.80 0.80 70
Training vgg with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6832528829574585
Epoch 2/10, Loss: 1.4907646894454956
Epoch 3/10, Loss: 1.3612913131713866
Epoch 4/10, Loss: 1.1922033786773683
Epoch 5/10, Loss: 0.9819200515747071
Epoch 6/10, Loss: 0.7627175211906433
Epoch 7/10, Loss: 0.6196128606796265
Epoch 8/10, Loss: 0.4351759314537048
Epoch 9/10, Loss: 0.3032832741737366
Epoch 10/10, Loss: 0.20180921554565429
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.84 0.84 0.84 19
Pipes 1.00 0.88 0.94 17
Rockets 0.83 0.91 0.87 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.86 70
macro avg 0.87 0.86 0.86 70
weighted avg 0.88 0.86 0.86 70
Training vgg with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.715203332901001
Epoch 2/10, Loss: 1.5676400423049928
Epoch 3/10, Loss: 1.3948649883270263
Epoch 4/10, Loss: 1.2669626951217652
Epoch 5/10, Loss: 1.0859084486961366
Epoch 6/10, Loss: 0.9391543745994568
Epoch 7/10, Loss: 0.67564377784729
Epoch 8/10, Loss: 0.5362069487571717
Epoch 9/10, Loss: 0.4024223148822784
Epoch 10/10, Loss: 0.31960909962654116
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.82 0.95 0.88 19
Pipes 1.00 0.82 0.90 17
Rockets 0.69 1.00 0.81 11
Vehicles 1.00 0.42 0.59 12
accuracy 0.84 70
macro avg 0.87 0.84 0.82 70
weighted avg 0.88 0.84 0.83 70
Training vgg with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 2028992403.417668
Epoch 2/10, Loss: 2861.0455215454103
Epoch 3/10, Loss: 1209.3464515686035
Epoch 4/10, Loss: 158.0430154800415
Epoch 5/10, Loss: 501.77315015792846
Epoch 6/10, Loss: 3.8782601356506348
Epoch 7/10, Loss: 2.0094608545303343
Epoch 8/10, Loss: 2.4002161026000977
Epoch 9/10, Loss: 1.6125469207763672
Epoch 10/10, Loss: 1.556179451942444
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.27 0.79 0.40 19
Pipes 1.00 0.00 0.00 17
Rockets 0.14 0.18 0.16 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.24 70
macro avg 0.68 0.19 0.11 70
weighted avg 0.67 0.24 0.13 70
Training vgg with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 119908795.54155143
Epoch 2/10, Loss: 24869.317602539064
Epoch 3/10, Loss: 4570.414233398437
Epoch 4/10, Loss: 541.610119342804
Epoch 5/10, Loss: 272.7794229507446
Epoch 6/10, Loss: 66.9474967956543
Epoch 7/10, Loss: 18.528394079208375
Epoch 8/10, Loss: 2.4209301233291627
Epoch 9/10, Loss: 1.7147159099578857
Epoch 10/10, Loss: 1.6344566583633422
Accuracy: 14.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 0.33 0.06 0.10 17
Rockets 0.13 0.82 0.23 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.14 70
macro avg 0.69 0.18 0.07 70
weighted avg 0.70 0.14 0.06 70
Training vgg with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 970358529.4467418
Epoch 2/10, Loss: 21255.554708099364
Epoch 3/10, Loss: 1126.8303705215453
Epoch 4/10, Loss: 51.200729560852054
Epoch 5/10, Loss: 69.26891746520997
Epoch 6/10, Loss: 22.745436239242554
Epoch 7/10, Loss: 5.15196795463562
Epoch 8/10, Loss: 2.2223118782043456
Epoch 9/10, Loss: 1.8328459978103637
Epoch 10/10, Loss: 1.6405266761779784
Accuracy: 12.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.08 0.18 0.11 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.14 0.55 0.22 11
Vehicles 1.00 0.08 0.15 12
accuracy 0.13 70
macro avg 0.64 0.16 0.10 70
weighted avg 0.72 0.13 0.08 70
Training vgg with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 2908055836.982587
Epoch 2/10, Loss: 2389.5009907245635
Epoch 3/10, Loss: 4453.9502624511715
Epoch 4/10, Loss: 1949.7208374023437
Epoch 5/10, Loss: 367.00809917449953
Epoch 6/10, Loss: 219.44107666015626
Epoch 7/10, Loss: 12.583441686630248
Epoch 8/10, Loss: 164.51255378723144
Epoch 9/10, Loss: 499.9480127811432
Epoch 10/10, Loss: 4.058997535705567
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 0.20 0.06 0.09 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.15 0.83 0.26 12
accuracy 0.16 70
macro avg 0.67 0.18 0.07 70
weighted avg 0.66 0.16 0.07 70
Training vgg with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 104974941.49721578
Epoch 2/10, Loss: 168.72383284568787
Epoch 3/10, Loss: 1.6501238346099854
Epoch 4/10, Loss: 1.629315233230591
Epoch 5/10, Loss: 1.630087685585022
Epoch 6/10, Loss: 1.634611463546753
Epoch 7/10, Loss: 508.8755687952042
Epoch 8/10, Loss: 2882.735949707031
Epoch 9/10, Loss: 3684.245947265625
Epoch 10/10, Loss: 63.1217383146286
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 0.24 1.00 0.39 17
Rockets 1.00 0.00 0.00 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.24 70
macro avg 0.85 0.20 0.08 70
weighted avg 0.82 0.24 0.09 70
Training vgg with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1885608212.947678
Epoch 2/10, Loss: 624.7573343515396
Epoch 3/10, Loss: 2.373706269264221
Epoch 4/10, Loss: 1.6397744178771974
Epoch 5/10, Loss: 1.6248837232589721
Epoch 6/10, Loss: 1.6301882982254028
Epoch 7/10, Loss: 1.5984672546386718
Epoch 8/10, Loss: 67.65103659629821
Epoch 9/10, Loss: 27.566797828674318
Epoch 10/10, Loss: 104.19730923175811
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.16 1.00 0.27 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.16 70
macro avg 0.83 0.20 0.05 70
weighted avg 0.87 0.16 0.04 70
Training vgg with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 714599409.6781927
Epoch 2/10, Loss: 2835.2301990270616
Epoch 3/10, Loss: 24398.98903236389
Epoch 4/10, Loss: 7135.005842590332
Epoch 5/10, Loss: 2472.661734199524
Epoch 6/10, Loss: 2460.4491004943848
Epoch 7/10, Loss: 60.0299412727356
Epoch 8/10, Loss: 75.29374511241913
Epoch 9/10, Loss: 54.57363224029541
Epoch 10/10, Loss: 8.088847732543945
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 489548643.46316373
Epoch 2/10, Loss: 4118.972521972656
Epoch 3/10, Loss: 10199.477755737305
Epoch 4/10, Loss: 1569.2827941894532
Epoch 5/10, Loss: 269.21624755859375
Epoch 6/10, Loss: 7.205962324142456
Epoch 7/10, Loss: 2.2182069778442384
Epoch 8/10, Loss: 1.6235222101211548
Epoch 9/10, Loss: 1.758490800857544
Epoch 10/10, Loss: 2.6945406675338743
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.16 1.00 0.27 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.16 70
macro avg 0.83 0.20 0.05 70
weighted avg 0.87 0.16 0.04 70
Training vgg with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 636158338.3560121
Epoch 2/10, Loss: 706.2195735931397
Epoch 3/10, Loss: 1837.6296127319335
Epoch 4/10, Loss: 137.6737968444824
Epoch 5/10, Loss: 73.06715888977051
Epoch 6/10, Loss: 4.1793855905532835
Epoch 7/10, Loss: 174.4966163635254
Epoch 8/10, Loss: 137.30274467468263
Epoch 9/10, Loss: 16.435970115661622
Epoch 10/10, Loss: 2.9101235628128053
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.16 1.00 0.27 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.16 70
macro avg 0.83 0.20 0.05 70
weighted avg 0.87 0.16 0.04 70
Training vgg with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6982465187708538
Epoch 2/10, Loss: 1.6616496245066326
Epoch 3/10, Loss: 1.5802560846010845
Epoch 4/10, Loss: 1.6645836896366544
Epoch 5/10, Loss: 1.519740362962087
Epoch 6/10, Loss: 1.36948029200236
Epoch 7/10, Loss: 1.225911365614997
Epoch 8/10, Loss: 1.210207333167394
Epoch 9/10, Loss: 1.262610958682166
Epoch 10/10, Loss: 1.1214225325319502
Accuracy: 40.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.60 0.55 0.57 11
Mines 0.50 0.42 0.46 19
Pipes 0.00 0.00 0.00 17
Rockets 0.26 0.91 0.41 11
Vehicles 1.00 0.33 0.50 12
accuracy 0.40 70
macro avg 0.47 0.44 0.39 70
weighted avg 0.44 0.40 0.36 70
Training vgg with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6673882802327473
Epoch 2/10, Loss: 1.6205346915456984
Epoch 3/10, Loss: 1.5996873246298895
Epoch 4/10, Loss: 1.5274276402261522
Epoch 5/10, Loss: 1.457391017013126
Epoch 6/10, Loss: 1.5935342113176982
Epoch 7/10, Loss: 1.453335828251309
Epoch 8/10, Loss: 1.3877670102649264
Epoch 9/10, Loss: 1.2629124058617487
Epoch 10/10, Loss: 1.0786221226056416
Accuracy: 38.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.86 0.55 0.67 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.22 1.00 0.37 11
Vehicles 0.71 0.83 0.77 12
accuracy 0.39 70
macro avg 0.76 0.48 0.36 70
weighted avg 0.81 0.39 0.29 70
Training vgg with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6513294180234273
Epoch 2/10, Loss: 1.595942133002811
Epoch 3/10, Loss: 1.6512892180018954
Epoch 4/10, Loss: 1.7439917590883043
Epoch 5/10, Loss: 1.596138682630327
Epoch 6/10, Loss: 1.5880258083343506
Epoch 7/10, Loss: 1.4672627515263028
Epoch 8/10, Loss: 1.4226598805851407
Epoch 9/10, Loss: 1.3751230769687228
Epoch 10/10, Loss: 1.7467521561516657
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.33 0.09 0.14 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.16 0.92 0.28 12
accuracy 0.17 70
macro avg 0.70 0.20 0.08 70
weighted avg 0.75 0.17 0.07 70
Training vgg with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6734251181284587
Epoch 2/10, Loss: 1.614942318863339
Epoch 3/10, Loss: 1.6732452379332647
Epoch 4/10, Loss: 1.6105937759081523
Epoch 5/10, Loss: 1.6315249403317769
Epoch 6/10, Loss: 1.6319555573993259
Epoch 7/10, Loss: 1.6153732339541118
Epoch 8/10, Loss: 1.604878008365631
Epoch 9/10, Loss: 1.6188087728288438
Epoch 10/10, Loss: 1.6134450369411044
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6806160277790494
Epoch 2/10, Loss: 1.6188100708855524
Epoch 3/10, Loss: 1.526063707139757
Epoch 4/10, Loss: 1.510090662373437
Epoch 5/10, Loss: 1.5268548197216458
Epoch 6/10, Loss: 1.3965316083696153
Epoch 7/10, Loss: 1.3133435646692913
Epoch 8/10, Loss: 1.0745623376634386
Epoch 9/10, Loss: 1.0081373949845631
Epoch 10/10, Loss: 0.8375465820233027
Accuracy: 72.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.55 0.60 11
Mines 0.74 0.74 0.74 19
Pipes 0.89 0.94 0.91 17
Rockets 0.58 0.64 0.61 11
Vehicles 0.67 0.67 0.67 12
accuracy 0.73 70
macro avg 0.71 0.71 0.71 70
weighted avg 0.73 0.73 0.73 70
Training vgg with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.645951920085483
Epoch 2/10, Loss: 1.6170910729302301
Epoch 3/10, Loss: 1.5900903873973422
Epoch 4/10, Loss: 1.5900455647044711
Epoch 5/10, Loss: 1.501981450451745
Epoch 6/10, Loss: 1.2805426584349737
Epoch 7/10, Loss: 1.1628987987836201
Epoch 8/10, Loss: 1.1137003865506914
Epoch 9/10, Loss: 0.9921364270978503
Epoch 10/10, Loss: 0.7976515409019258
Accuracy: 61.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.58 0.64 0.61 11
Mines 0.69 0.47 0.56 19
Pipes 1.00 0.59 0.74 17
Rockets 0.47 0.64 0.54 11
Vehicles 0.50 0.83 0.62 12
accuracy 0.61 70
macro avg 0.65 0.63 0.62 70
weighted avg 0.68 0.61 0.62 70
Training vgg with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6801997621854146
Epoch 2/10, Loss: 1.6327036619186401
Epoch 3/10, Loss: 1.6228849556710985
Epoch 4/10, Loss: 1.5907482504844666
Epoch 5/10, Loss: 1.5076125462849934
Epoch 6/10, Loss: 1.2995250821113586
Epoch 7/10, Loss: 1.3614640302128262
Epoch 8/10, Loss: 1.2564365300867293
Epoch 9/10, Loss: 1.2279689444435968
Epoch 10/10, Loss: 1.1957889993985493
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.23 0.64 0.33 11
Vehicles 0.26 0.83 0.39 12
accuracy 0.24 70
macro avg 0.70 0.29 0.15 70
weighted avg 0.75 0.24 0.12 70
Training vgg with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6700162556436327
Epoch 2/10, Loss: 1.5827150146166484
Epoch 3/10, Loss: 1.4409075180689495
Epoch 4/10, Loss: 1.4132649766074286
Epoch 5/10, Loss: 1.3073055512375302
Epoch 6/10, Loss: 1.4827616181638505
Epoch 7/10, Loss: 1.2305364112059276
Epoch 8/10, Loss: 1.031541258096695
Epoch 9/10, Loss: 1.1544988751411438
Epoch 10/10, Loss: 0.9366370605097877
Accuracy: 48.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.64 0.64 11
Mines 0.60 0.47 0.53 19
Pipes 0.67 0.12 0.20 17
Rockets 0.30 0.73 0.42 11
Vehicles 0.57 0.67 0.62 12
accuracy 0.49 70
macro avg 0.55 0.52 0.48 70
weighted avg 0.57 0.49 0.46 70
Training vgg with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6996383402082655
Epoch 2/10, Loss: 1.6071629656685724
Epoch 3/10, Loss: 1.575604134135776
Epoch 4/10, Loss: 1.5955291589101155
Epoch 5/10, Loss: 1.5151545604070027
Epoch 6/10, Loss: 1.3059231572681003
Epoch 7/10, Loss: 1.0984695338540607
Epoch 8/10, Loss: 1.157688472006056
Epoch 9/10, Loss: 1.0468785050842497
Epoch 10/10, Loss: 0.8881755206320021
Accuracy: 51.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.18 0.27 11
Mines 1.00 0.21 0.35 19
Pipes 0.64 0.82 0.72 17
Rockets 0.24 0.55 0.33 11
Vehicles 0.67 0.83 0.74 12
accuracy 0.51 70
macro avg 0.61 0.52 0.48 70
weighted avg 0.66 0.51 0.49 70
Training vgg with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.5904141068458557
Epoch 2/10, Loss: 1.2100605799092188
Epoch 3/10, Loss: 0.7867967949973212
Epoch 4/10, Loss: 0.4558102563023567
Epoch 5/10, Loss: 0.3485914369424184
Epoch 6/10, Loss: 0.2375481580901477
Epoch 7/10, Loss: 0.16869361305402386
Epoch 8/10, Loss: 0.047353623227940664
Epoch 9/10, Loss: 0.025672882200322218
Epoch 10/10, Loss: 0.011679477899128364
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.80 0.84 0.82 19
Pipes 1.00 0.88 0.94 17
Rockets 0.90 0.82 0.86 11
Vehicles 0.71 0.83 0.77 12
accuracy 0.86 70
macro avg 0.86 0.86 0.86 70
weighted avg 0.87 0.86 0.86 70
Training vgg with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.636500981118944
Epoch 2/10, Loss: 1.3178526560465496
Epoch 3/10, Loss: 0.9668066004912058
Epoch 4/10, Loss: 0.590247619483206
Epoch 5/10, Loss: 0.39994533194435966
Epoch 6/10, Loss: 0.22700286801490518
Epoch 7/10, Loss: 0.20175345417939955
Epoch 8/10, Loss: 0.11008325048411886
Epoch 9/10, Loss: 0.05200886964384052
Epoch 10/10, Loss: 0.055611006773283914
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.94 0.84 0.89 19
Pipes 1.00 0.94 0.97 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.89 70
macro avg 0.89 0.89 0.88 70
weighted avg 0.91 0.89 0.89 70
Training vgg with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.640166203180949
Epoch 2/10, Loss: 1.271866242090861
Epoch 3/10, Loss: 0.9103218681282468
Epoch 4/10, Loss: 0.675731951991717
Epoch 5/10, Loss: 0.4340719146033128
Epoch 6/10, Loss: 0.33843238320615554
Epoch 7/10, Loss: 0.13124566121647754
Epoch 8/10, Loss: 0.08975760509686855
Epoch 9/10, Loss: 0.11148620872861809
Epoch 10/10, Loss: 0.21999152128895125
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.73 0.84 11
Mines 0.89 0.84 0.86 19
Pipes 1.00 0.88 0.94 17
Rockets 0.69 1.00 0.81 11
Vehicles 0.69 0.75 0.72 12
accuracy 0.84 70
macro avg 0.85 0.84 0.84 70
weighted avg 0.87 0.84 0.85 70
Training vgg with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.566300868988037
Epoch 2/10, Loss: 1.1928699678844876
Epoch 3/10, Loss: 0.7628961834642622
Epoch 4/10, Loss: 0.45236870066987145
Epoch 5/10, Loss: 0.2902586923705207
Epoch 6/10, Loss: 0.20014169936378798
Epoch 7/10, Loss: 0.10316568292263481
Epoch 8/10, Loss: 0.06550061609596014
Epoch 9/10, Loss: 0.04937525757769537
Epoch 10/10, Loss: 0.06704761411270334
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.94 0.84 0.89 19
Pipes 1.00 0.88 0.94 17
Rockets 0.83 0.91 0.87 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.89 70
macro avg 0.88 0.89 0.88 70
weighted avg 0.89 0.89 0.89 70
Training vgg with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.628896587424808
Epoch 2/10, Loss: 1.2840708659754858
Epoch 3/10, Loss: 0.8797703484694163
Epoch 4/10, Loss: 0.4881972811288304
Epoch 5/10, Loss: 0.31887489143345094
Epoch 6/10, Loss: 0.19783791630632347
Epoch 7/10, Loss: 0.10925923412044843
Epoch 8/10, Loss: 0.06030991753666765
Epoch 9/10, Loss: 0.05851564434770909
Epoch 10/10, Loss: 0.028731141058314178
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.76 1.00 0.86 19
Pipes 1.00 0.82 0.90 17
Rockets 0.90 0.82 0.86 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.89 70
macro avg 0.92 0.88 0.89 70
weighted avg 0.91 0.89 0.89 70
Training vgg with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6462984813584223
Epoch 2/10, Loss: 1.336561295721266
Epoch 3/10, Loss: 0.9922929174370236
Epoch 4/10, Loss: 0.7960722711351182
Epoch 5/10, Loss: 0.5782145178980298
Epoch 6/10, Loss: 0.29298451501462197
Epoch 7/10, Loss: 0.24641781703879437
Epoch 8/10, Loss: 0.09824587663428651
Epoch 9/10, Loss: 0.06894018852876292
Epoch 10/10, Loss: 0.052700280082515545
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.82 0.74 0.78 19
Pipes 0.94 0.88 0.91 17
Rockets 0.77 0.91 0.83 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.84 70
macro avg 0.84 0.85 0.84 70
weighted avg 0.85 0.84 0.84 70
Training vgg with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5508741007910833
Epoch 2/10, Loss: 1.1043701966603596
Epoch 3/10, Loss: 0.6716455833779441
Epoch 4/10, Loss: 0.4541823011305597
Epoch 5/10, Loss: 0.3088941764500406
Epoch 6/10, Loss: 0.11857941658753487
Epoch 7/10, Loss: 0.07661855676107937
Epoch 8/10, Loss: 0.039526795813192926
Epoch 9/10, Loss: 0.022713475708668638
Epoch 10/10, Loss: 0.012087346310080547
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 1.00 1.00 11
Mines 0.83 1.00 0.90 19
Pipes 1.00 0.88 0.94 17
Rockets 0.83 0.91 0.87 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.91 70
macro avg 0.93 0.91 0.91 70
weighted avg 0.93 0.91 0.91 70
Training vgg with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5705543955167134
Epoch 2/10, Loss: 1.2233330541186862
Epoch 3/10, Loss: 0.864209277762307
Epoch 4/10, Loss: 0.5658385016851954
Epoch 5/10, Loss: 0.3282640402515729
Epoch 6/10, Loss: 0.23831061253117192
Epoch 7/10, Loss: 0.0865202381586035
Epoch 8/10, Loss: 0.05754157490769608
Epoch 9/10, Loss: 0.038327904495721064
Epoch 10/10, Loss: 0.02634107441796611
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.78 0.95 0.86 19
Pipes 1.00 0.88 0.94 17
Rockets 0.91 0.91 0.91 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.89 70
macro avg 0.91 0.88 0.89 70
weighted avg 0.90 0.89 0.89 70
Training vgg with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6486185590426128
Epoch 2/10, Loss: 1.3837357891930475
Epoch 3/10, Loss: 1.078568653927909
Epoch 4/10, Loss: 0.6838307107488314
Epoch 5/10, Loss: 0.517207045522001
Epoch 6/10, Loss: 0.3205403900808758
Epoch 7/10, Loss: 0.2436110863669051
Epoch 8/10, Loss: 0.14548323675990105
Epoch 9/10, Loss: 0.08190723353375991
Epoch 10/10, Loss: 0.1394505786140346
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 0.94 0.97 17
Rockets 0.82 0.82 0.82 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.90 70
macro avg 0.89 0.89 0.89 70
weighted avg 0.91 0.90 0.90 70
Training vgg with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 4366.149460891883
Epoch 2/10, Loss: 1.9507553908560011
Epoch 3/10, Loss: 1.654340664545695
Epoch 4/10, Loss: 2.2274950279129877
Epoch 5/10, Loss: 1.6237944232092962
Epoch 6/10, Loss: 1.583124770058526
Epoch 7/10, Loss: 1.6433905098173354
Epoch 8/10, Loss: 1.5348406897650824
Epoch 9/10, Loss: 1.5256852904955547
Epoch 10/10, Loss: 1.5889254675971136
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.23 0.73 0.35 11
Vehicles 0.26 0.75 0.38 12
accuracy 0.24 70
macro avg 0.70 0.30 0.15 70
weighted avg 0.75 0.24 0.12 70
Training vgg with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 24960.531860066785
Epoch 2/10, Loss: 2.796283039781782
Epoch 3/10, Loss: 1.6475801467895508
Epoch 4/10, Loss: 1.6284122003449335
Epoch 5/10, Loss: 1.5174791945351496
Epoch 6/10, Loss: 2.1059074335628085
Epoch 7/10, Loss: 1.6563825011253357
Epoch 8/10, Loss: 1.5113956994480557
Epoch 9/10, Loss: 1.420275671614541
Epoch 10/10, Loss: 1.3822214172946081
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.18 1.00 0.31 11
Vehicles 0.11 0.08 0.10 12
accuracy 0.17 70
macro avg 0.46 0.22 0.08 70
weighted avg 0.45 0.17 0.07 70
Training vgg with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 25209.00156593985
Epoch 2/10, Loss: 2.010847694343991
Epoch 3/10, Loss: 1.5935459799236722
Epoch 4/10, Loss: 1.5713875889778137
Epoch 5/10, Loss: 1.5865998069445293
Epoch 6/10, Loss: 1.478160646226671
Epoch 7/10, Loss: 1.4486492938465543
Epoch 8/10, Loss: 1.3797201249334548
Epoch 9/10, Loss: 1.3468110760052998
Epoch 10/10, Loss: 1.3696851631005604
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.24 0.45 0.31 11
Mines 0.14 0.05 0.08 19
Pipes 1.00 0.00 0.00 17
Rockets 0.16 0.27 0.20 11
Vehicles 0.35 0.67 0.46 12
accuracy 0.24 70
macro avg 0.38 0.29 0.21 70
weighted avg 0.40 0.24 0.18 70
Training vgg with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 3693.7870470351645
Epoch 2/10, Loss: 1.6948135164048936
Epoch 3/10, Loss: 1.8275462985038757
Epoch 4/10, Loss: 1.6513536506228976
Epoch 5/10, Loss: 1.576115568478902
Epoch 6/10, Loss: 1.5507330099741619
Epoch 7/10, Loss: 1.6178376807106867
Epoch 8/10, Loss: 1.4276292655203078
Epoch 9/10, Loss: 1.7879447937011719
Epoch 10/10, Loss: 1.702069103717804
Accuracy: 30.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.30 0.27 0.29 11
Mines 0.44 0.21 0.29 19
Pipes 0.27 0.82 0.41 17
Rockets 1.00 0.00 0.00 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.30 70
macro avg 0.60 0.26 0.20 70
weighted avg 0.56 0.30 0.22 70
Training vgg with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 16313.785093989638
Epoch 2/10, Loss: 1.8575513892703586
Epoch 3/10, Loss: 1.6119472251998053
Epoch 4/10, Loss: 1.5818408992555406
Epoch 5/10, Loss: 1.5424144135581122
Epoch 6/10, Loss: 1.453476243548923
Epoch 7/10, Loss: 1.4023221400048997
Epoch 8/10, Loss: 1.5182733469539218
Epoch 9/10, Loss: 1.256955749458737
Epoch 10/10, Loss: 1.2367857197920482
Accuracy: 30.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.36 0.53 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.23 0.64 0.33 11
Vehicles 0.29 0.83 0.43 12
accuracy 0.30 70
macro avg 0.70 0.37 0.26 70
weighted avg 0.76 0.30 0.21 70
Training vgg with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 3046.4684087700316
Epoch 2/10, Loss: 1.8538312051031325
Epoch 3/10, Loss: 1.6611799332830641
Epoch 4/10, Loss: 1.6064173645443387
Epoch 5/10, Loss: 1.5396269030041165
Epoch 6/10, Loss: 1.661192536354065
Epoch 7/10, Loss: 1.4284320738580492
Epoch 8/10, Loss: 2.7888722485966153
Epoch 9/10, Loss: 1.660009741783142
Epoch 10/10, Loss: 1.5574386450979445
Accuracy: 21.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.18 0.64 0.29 11
Vehicles 0.25 0.67 0.36 12
accuracy 0.21 70
macro avg 0.69 0.26 0.13 70
weighted avg 0.74 0.21 0.11 70
Training vgg with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 70275.68814686273
Epoch 2/10, Loss: 6.869169202115801
Epoch 3/10, Loss: 1.7033196091651917
Epoch 4/10, Loss: 1.6150095793935988
Epoch 5/10, Loss: 2.353826171822018
Epoch 6/10, Loss: 1.582425011528863
Epoch 7/10, Loss: 1.605466041300032
Epoch 8/10, Loss: 1.8145736654599507
Epoch 9/10, Loss: 1.565390408039093
Epoch 10/10, Loss: 1.7054127520985074
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.24 0.73 0.36 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.18 0.27 0.21 11
Vehicles 0.05 0.08 0.06 12
accuracy 0.17 70
macro avg 0.49 0.22 0.13 70
weighted avg 0.59 0.17 0.10 70
Training vgg with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 2881.7363683978715
Epoch 2/10, Loss: 1.8837891684638128
Epoch 3/10, Loss: 2.12408181031545
Epoch 4/10, Loss: 2.0426487723986306
Epoch 5/10, Loss: 1.6030489868587918
Epoch 6/10, Loss: 2.053523990843031
Epoch 7/10, Loss: 1.6689946121639676
Epoch 8/10, Loss: 1.6172120703591242
Epoch 9/10, Loss: 1.6085401442315843
Epoch 10/10, Loss: 1.5910643802748785
Accuracy: 20.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.18 0.31 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.17 0.18 0.17 11
Vehicles 0.18 0.83 0.29 12
accuracy 0.20 70
macro avg 0.67 0.24 0.16 70
weighted avg 0.73 0.20 0.13 70
Training vgg with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 89821.72040471103
Epoch 2/10, Loss: 1.866511881351471
Epoch 3/10, Loss: 1.6414807041486104
Epoch 4/10, Loss: 1.6140738394525316
Epoch 5/10, Loss: 1.6119093630048964
Epoch 6/10, Loss: 1.6304769317309062
Epoch 7/10, Loss: 1.5839224060376484
Epoch 8/10, Loss: 1.625428683227963
Epoch 9/10, Loss: 1.7191293173366122
Epoch 10/10, Loss: 1.3725623885790508
Accuracy: 30.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.00 0.00 0.00 11
Mines 0.31 0.21 0.25 19
Pipes 0.50 0.24 0.32 17
Rockets 0.23 0.82 0.35 11
Vehicles 0.50 0.33 0.40 12
accuracy 0.30 70
macro avg 0.31 0.32 0.26 70
weighted avg 0.33 0.30 0.27 70
Training vgg with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6448500686221652
Epoch 2/10, Loss: 1.6522892978456285
Epoch 3/10, Loss: 1.6248199277453952
Epoch 4/10, Loss: 1.6067642900678847
Epoch 5/10, Loss: 1.5920743809805975
Epoch 6/10, Loss: 1.6696217589908176
Epoch 7/10, Loss: 1.6176714102427165
Epoch 8/10, Loss: 1.6193298233879938
Epoch 9/10, Loss: 1.572261677847968
Epoch 10/10, Loss: 1.4694137573242188
Accuracy: 35.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.35 1.00 0.52 11
Mines 0.31 0.58 0.41 19
Pipes 1.00 0.00 0.00 17
Rockets 0.00 0.00 0.00 11
Vehicles 1.00 0.25 0.40 12
accuracy 0.36 70
macro avg 0.53 0.37 0.27 70
weighted avg 0.56 0.36 0.26 70
Training vgg with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.657494650946723
Epoch 2/10, Loss: 1.6394293308258057
Epoch 3/10, Loss: 1.486623658074273
Epoch 4/10, Loss: 1.5243725644217596
Epoch 5/10, Loss: 1.3140421443515353
Epoch 6/10, Loss: 1.1409916016790602
Epoch 7/10, Loss: 1.1440085570017497
Epoch 8/10, Loss: 1.123010026084052
Epoch 9/10, Loss: 0.9760061568684049
Epoch 10/10, Loss: 0.8453173637390137
Accuracy: 52.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.70 0.64 0.67 11
Mines 0.60 0.16 0.25 19
Pipes 0.39 1.00 0.56 17
Rockets 0.75 0.27 0.40 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.53 70
macro avg 0.69 0.53 0.52 70
weighted avg 0.66 0.53 0.50 70
Training vgg with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6575574345058866
Epoch 2/10, Loss: 1.5794461965560913
Epoch 3/10, Loss: 1.5800513823827107
Epoch 4/10, Loss: 1.5157761573791504
Epoch 5/10, Loss: 1.360454519589742
Epoch 6/10, Loss: 1.2468988762961493
Epoch 7/10, Loss: 1.2300046218766107
Epoch 8/10, Loss: 0.971009847190645
Epoch 9/10, Loss: 1.0289027094841003
Epoch 10/10, Loss: 0.7514722877078586
Accuracy: 60.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.73 0.70 11
Mines 0.64 0.47 0.55 19
Pipes 0.60 0.88 0.71 17
Rockets 0.20 0.09 0.12 11
Vehicles 0.64 0.75 0.69 12
accuracy 0.60 70
macro avg 0.55 0.58 0.55 70
weighted avg 0.57 0.60 0.57 70
Training vgg with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.7852588891983032
Epoch 2/10, Loss: 1.6459140512678359
Epoch 3/10, Loss: 1.6102327505747478
Epoch 4/10, Loss: 1.5894842545191448
Epoch 5/10, Loss: 1.4453932444254558
Epoch 6/10, Loss: 1.3451283507876926
Epoch 7/10, Loss: 1.1767993105782404
Epoch 8/10, Loss: 1.2597646713256836
Epoch 9/10, Loss: 0.9937188161744012
Epoch 10/10, Loss: 1.0257547828886244
Accuracy: 40.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.42 0.45 0.43 11
Mines 0.36 0.47 0.41 19
Pipes 1.00 0.00 0.00 17
Rockets 0.31 0.36 0.33 11
Vehicles 0.50 0.83 0.62 12
accuracy 0.40 70
macro avg 0.52 0.43 0.36 70
weighted avg 0.54 0.40 0.34 70
Training vgg with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7071207364400227
Epoch 2/10, Loss: 1.621029602156745
Epoch 3/10, Loss: 1.603797607951694
Epoch 4/10, Loss: 1.5687589512930975
Epoch 5/10, Loss: 1.4241188367207844
Epoch 6/10, Loss: 1.292122655444675
Epoch 7/10, Loss: 1.1168725689252217
Epoch 8/10, Loss: 1.2555450863308377
Epoch 9/10, Loss: 1.2105268504884508
Epoch 10/10, Loss: 1.1653722325960796
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.18 0.31 11
Mines 0.36 0.47 0.41 19
Pipes 0.67 0.24 0.35 17
Rockets 0.00 0.00 0.00 11
Vehicles 0.31 0.92 0.47 12
accuracy 0.37 70
macro avg 0.47 0.36 0.31 70
weighted avg 0.47 0.37 0.32 70
Training vgg with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6836742162704468
Epoch 2/10, Loss: 1.563628077507019
Epoch 3/10, Loss: 1.5605504247877333
Epoch 4/10, Loss: 1.4505157669385274
Epoch 5/10, Loss: 1.4985356860690646
Epoch 6/10, Loss: 1.2574789391623602
Epoch 7/10, Loss: 1.2057049009535048
Epoch 8/10, Loss: 1.0433456765280829
Epoch 9/10, Loss: 0.9432392650180392
Epoch 10/10, Loss: 1.0929276280932956
Accuracy: 41.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.38 0.91 0.54 11
Mines 0.67 0.21 0.32 19
Pipes 1.00 0.00 0.00 17
Rockets 0.23 0.64 0.34 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.41 70
macro avg 0.66 0.48 0.40 70
weighted avg 0.69 0.41 0.36 70
Training vgg with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6482733223173354
Epoch 2/10, Loss: 1.5706402725643582
Epoch 3/10, Loss: 1.6280808183881972
Epoch 4/10, Loss: 1.79582855436537
Epoch 5/10, Loss: 1.5617588361104329
Epoch 6/10, Loss: 1.5609029796388414
Epoch 7/10, Loss: 1.4814116292529635
Epoch 8/10, Loss: 1.433425201310052
Epoch 9/10, Loss: 1.3110306395424738
Epoch 10/10, Loss: 1.1110599835713704
Accuracy: 42.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.38 0.73 0.50 11
Mines 0.36 0.21 0.27 19
Pipes 0.62 0.29 0.40 17
Rockets 0.31 0.45 0.37 11
Vehicles 0.57 0.67 0.62 12
accuracy 0.43 70
macro avg 0.45 0.47 0.43 70
weighted avg 0.46 0.43 0.41 70
Training vgg with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6941865417692397
Epoch 2/10, Loss: 1.5941492716471355
Epoch 3/10, Loss: 1.6054327620400324
Epoch 4/10, Loss: 1.5240650044547186
Epoch 5/10, Loss: 1.4641642835405138
Epoch 6/10, Loss: 1.3044150935278997
Epoch 7/10, Loss: 1.3794188896814983
Epoch 8/10, Loss: 1.1525456640455458
Epoch 9/10, Loss: 1.27898989783393
Epoch 10/10, Loss: 1.1527316835191515
Accuracy: 40.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.55 0.52 11
Mines 1.00 0.00 0.00 19
Pipes 0.50 0.65 0.56 17
Rockets 0.40 0.55 0.46 11
Vehicles 0.24 0.42 0.30 12
accuracy 0.40 70
macro avg 0.53 0.43 0.37 70
weighted avg 0.58 0.40 0.34 70
Training vgg with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.7922747797436185
Epoch 2/10, Loss: 1.6355729897816975
Epoch 3/10, Loss: 1.618459979693095
Epoch 4/10, Loss: 1.603157189157274
Epoch 5/10, Loss: 1.6130985816319783
Epoch 6/10, Loss: 1.6026473310258653
Epoch 7/10, Loss: 1.5981163846121893
Epoch 8/10, Loss: 1.4780850145551894
Epoch 9/10, Loss: 1.5756987598207262
Epoch 10/10, Loss: 1.6339894533157349
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.16 1.00 0.27 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.16 70
macro avg 0.83 0.20 0.05 70
weighted avg 0.87 0.16 0.04 70
Training vgg with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6016255352232192
Epoch 2/10, Loss: 1.4236213233735826
Epoch 3/10, Loss: 1.2277935875786676
Epoch 4/10, Loss: 0.9822639160686069
Epoch 5/10, Loss: 0.7183244691954719
Epoch 6/10, Loss: 0.5005818009376526
Epoch 7/10, Loss: 0.4211261322100957
Epoch 8/10, Loss: 0.26694685800207985
Epoch 9/10, Loss: 0.1521418574783537
Epoch 10/10, Loss: 0.09037293079826567
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.80 0.84 0.82 19
Pipes 1.00 0.88 0.94 17
Rockets 0.89 0.73 0.80 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.84 70
macro avg 0.84 0.84 0.84 70
weighted avg 0.85 0.84 0.84 70
Training vgg with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6587730646133423
Epoch 2/10, Loss: 1.4417022864023845
Epoch 3/10, Loss: 1.279136962360806
Epoch 4/10, Loss: 1.0291928980085585
Epoch 5/10, Loss: 0.7923738426632352
Epoch 6/10, Loss: 0.596847653388977
Epoch 7/10, Loss: 0.44132786989212036
Epoch 8/10, Loss: 0.3403899355067147
Epoch 9/10, Loss: 0.21133407867617077
Epoch 10/10, Loss: 0.16718260695536932
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 1.00 0.79 0.88 19
Pipes 1.00 0.94 0.97 17
Rockets 0.67 0.91 0.77 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.86 70
macro avg 0.86 0.86 0.85 70
weighted avg 0.89 0.86 0.86 70
Training vgg with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.7092130449083116
Epoch 2/10, Loss: 1.4942990806367662
Epoch 3/10, Loss: 1.3368862867355347
Epoch 4/10, Loss: 1.1402910086843703
Epoch 5/10, Loss: 0.9405075709025065
Epoch 6/10, Loss: 0.7764739460415311
Epoch 7/10, Loss: 0.5804632107416788
Epoch 8/10, Loss: 0.45762431290414596
Epoch 9/10, Loss: 0.30598554346296525
Epoch 10/10, Loss: 0.294730710486571
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.58 1.00 0.73 11
Mines 0.82 0.74 0.78 19
Pipes 1.00 0.82 0.90 17
Rockets 0.75 0.82 0.78 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.80 70
macro avg 0.83 0.81 0.80 70
weighted avg 0.85 0.80 0.81 70
Training vgg with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6545428832372029
Epoch 2/10, Loss: 1.4278572532865736
Epoch 3/10, Loss: 1.2551936043633356
Epoch 4/10, Loss: 1.011820567978753
Epoch 5/10, Loss: 0.8390690883000692
Epoch 6/10, Loss: 0.5754407611158159
Epoch 7/10, Loss: 0.3793136907948388
Epoch 8/10, Loss: 0.3347195726301935
Epoch 9/10, Loss: 0.18386633859740364
Epoch 10/10, Loss: 0.09882308252983624
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.77 0.89 0.83 19
Pipes 1.00 0.82 0.90 17
Rockets 0.75 0.82 0.78 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.84 70
macro avg 0.86 0.84 0.84 70
weighted avg 0.87 0.84 0.84 70
Training vgg with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6425402296913996
Epoch 2/10, Loss: 1.4654328690634832
Epoch 3/10, Loss: 1.2644542588127985
Epoch 4/10, Loss: 1.048438482814365
Epoch 5/10, Loss: 0.8151620626449585
Epoch 6/10, Loss: 0.5927717387676239
Epoch 7/10, Loss: 0.5211804575390286
Epoch 8/10, Loss: 0.3449041330152088
Epoch 9/10, Loss: 0.24289229760567346
Epoch 10/10, Loss: 0.1542693885664145
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 0.89 0.84 0.86 19
Pipes 1.00 0.88 0.94 17
Rockets 0.82 0.82 0.82 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.87 70
macro avg 0.88 0.88 0.87 70
weighted avg 0.89 0.87 0.87 70
Training vgg with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.7408348189459906
Epoch 2/10, Loss: 1.5353400707244873
Epoch 3/10, Loss: 1.336205734146966
Epoch 4/10, Loss: 1.1217745476298862
Epoch 5/10, Loss: 0.8929708467589484
Epoch 6/10, Loss: 0.7118714253107706
Epoch 7/10, Loss: 0.5688604811827341
Epoch 8/10, Loss: 0.5405108018053902
Epoch 9/10, Loss: 0.4118061463038127
Epoch 10/10, Loss: 0.2440691755877601
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.77 0.89 0.83 19
Pipes 0.94 0.88 0.91 17
Rockets 0.88 0.64 0.74 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.83 70
macro avg 0.85 0.81 0.82 70
weighted avg 0.85 0.83 0.83 70
Training vgg with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5928537315792508
Epoch 2/10, Loss: 1.4210431840684679
Epoch 3/10, Loss: 1.1968236433135138
Epoch 4/10, Loss: 0.9497484167416891
Epoch 5/10, Loss: 0.6955799361069998
Epoch 6/10, Loss: 0.4932530423005422
Epoch 7/10, Loss: 0.37076207829846275
Epoch 8/10, Loss: 0.20957063221269184
Epoch 9/10, Loss: 0.15505364537239075
Epoch 10/10, Loss: 0.11875871610310343
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 0.87 0.68 0.76 19
Pipes 0.94 0.94 0.94 17
Rockets 0.75 0.82 0.78 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.83 70
macro avg 0.83 0.84 0.82 70
weighted avg 0.84 0.83 0.83 70
Training vgg with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7023303906122844
Epoch 2/10, Loss: 1.4535603125890095
Epoch 3/10, Loss: 1.2999210490120783
Epoch 4/10, Loss: 1.100392805205451
Epoch 5/10, Loss: 0.8163392345110575
Epoch 6/10, Loss: 0.6405881543954214
Epoch 7/10, Loss: 0.45790425274107194
Epoch 8/10, Loss: 0.34146225452423096
Epoch 9/10, Loss: 0.29737478660212624
Epoch 10/10, Loss: 0.1817509722378519
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.82 0.78 11
Mines 0.88 0.79 0.83 19
Pipes 1.00 0.88 0.94 17
Rockets 0.67 0.91 0.77 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.83 70
macro avg 0.82 0.83 0.82 70
weighted avg 0.85 0.83 0.83 70
Training vgg with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6845333046383328
Epoch 2/10, Loss: 1.5109941694471571
Epoch 3/10, Loss: 1.3222953346040514
Epoch 4/10, Loss: 1.1664609909057617
Epoch 5/10, Loss: 0.9963532951143053
Epoch 6/10, Loss: 0.7678324977556864
Epoch 7/10, Loss: 0.5481979681385888
Epoch 8/10, Loss: 0.43658841649691266
Epoch 9/10, Loss: 0.33660128050380284
Epoch 10/10, Loss: 0.2218256468574206
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 0.89 0.84 0.86 19
Pipes 1.00 0.88 0.94 17
Rockets 0.88 0.64 0.74 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.84 70
macro avg 0.84 0.84 0.83 70
weighted avg 0.86 0.84 0.84 70
Training vgg with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 24201.656350705358
Epoch 2/10, Loss: 1.7772059043248494
Epoch 3/10, Loss: 1.6194084882736206
Epoch 4/10, Loss: 1.594235274526808
Epoch 5/10, Loss: 1.6110128959019978
Epoch 6/10, Loss: 1.6392513381110296
Epoch 7/10, Loss: 1.6070922348234389
Epoch 8/10, Loss: 1.6409486399756537
Epoch 9/10, Loss: 1.5944681697421603
Epoch 10/10, Loss: 2.566860795021057
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.16 1.00 0.27 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.16 70
macro avg 0.83 0.20 0.05 70
weighted avg 0.87 0.16 0.04 70
Training vgg with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 49465.736600637436
Epoch 2/10, Loss: 2.2281605535083346
Epoch 3/10, Loss: 1.7066367732153997
Epoch 4/10, Loss: 1.8869622151056926
Epoch 5/10, Loss: 1.7566367122862074
Epoch 6/10, Loss: 1.5780682563781738
Epoch 7/10, Loss: 1.6152573691474066
Epoch 8/10, Loss: 1.5165882640414767
Epoch 9/10, Loss: 1.5342466433842976
Epoch 10/10, Loss: 1.388638178507487
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.35 0.55 0.43 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.20 0.91 0.32 11
Vehicles 0.50 0.08 0.14 12
accuracy 0.24 70
macro avg 0.61 0.31 0.18 70
weighted avg 0.69 0.24 0.14 70
Training vgg with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 11016.369269516734
Epoch 2/10, Loss: 2.0411688486735025
Epoch 3/10, Loss: 4.280878874990675
Epoch 4/10, Loss: 1.6562839216656156
Epoch 5/10, Loss: 2.0277820693122015
Epoch 6/10, Loss: 1.6353717380099826
Epoch 7/10, Loss: 1.695731308725145
Epoch 8/10, Loss: 1.5960997740427654
Epoch 9/10, Loss: 1.547534916136
Epoch 10/10, Loss: 1.5644645028644137
Accuracy: 25.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.09 0.15 11
Mines 0.20 0.16 0.18 19
Pipes 1.00 0.00 0.00 17
Rockets 0.22 0.45 0.29 11
Vehicles 0.30 0.75 0.43 12
accuracy 0.26 70
macro avg 0.44 0.29 0.21 70
weighted avg 0.46 0.26 0.19 70
Training vgg with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 118222.93307322926
Epoch 2/10, Loss: 4.0827098025216
Epoch 3/10, Loss: 1.6843571530448065
Epoch 4/10, Loss: 2.5486406882603965
Epoch 5/10, Loss: 1.5229188071356878
Epoch 6/10, Loss: 1.4253876871532865
Epoch 7/10, Loss: 1.839921898312039
Epoch 8/10, Loss: 1.3957095278633966
Epoch 9/10, Loss: 1.2476438019010756
Epoch 10/10, Loss: 1.2911331123775907
Accuracy: 35.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.40 0.55 0.46 11
Mines 0.43 0.32 0.36 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.18 0.31 11
Vehicles 0.28 0.92 0.43 12
accuracy 0.36 70
macro avg 0.62 0.39 0.31 70
weighted avg 0.63 0.36 0.29 70
Training vgg with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 12982.526863376299
Epoch 2/10, Loss: 22.38901228374905
Epoch 3/10, Loss: 1.6214377482732136
Epoch 4/10, Loss: 1.6118972963756986
Epoch 5/10, Loss: 2.283761223157247
Epoch 6/10, Loss: 1.6437415149476793
Epoch 7/10, Loss: 1.609828445646498
Epoch 8/10, Loss: 1.6041704681184557
Epoch 9/10, Loss: 1.5876507891549005
Epoch 10/10, Loss: 1.5928329626719158
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.16 0.82 0.26 11
Vehicles 0.23 0.25 0.24 12
accuracy 0.17 70
macro avg 0.68 0.21 0.10 70
weighted avg 0.74 0.17 0.08 70
Training vgg with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 9913.949157357216
Epoch 2/10, Loss: 4.211613456408183
Epoch 3/10, Loss: 1.6807812849680583
Epoch 4/10, Loss: 1.636521855990092
Epoch 5/10, Loss: 1.5897266334957547
Epoch 6/10, Loss: 1.5397803253597684
Epoch 7/10, Loss: 1.9264028072357178
Epoch 8/10, Loss: 1.6199039353264704
Epoch 9/10, Loss: 1.618403951327006
Epoch 10/10, Loss: 1.6025325457255046
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.12 0.09 0.11 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.16 0.83 0.27 12
accuracy 0.16 70
macro avg 0.66 0.18 0.08 70
weighted avg 0.72 0.16 0.06 70
Training vgg with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 62448.886621130834
Epoch 2/10, Loss: 1.8804634279674954
Epoch 3/10, Loss: 2.2631921503278942
Epoch 4/10, Loss: 1.725402553876241
Epoch 5/10, Loss: 1.610486176278856
Epoch 6/10, Loss: 2.036208152770996
Epoch 7/10, Loss: 2.4139480590820312
Epoch 8/10, Loss: 2.168880581855774
Epoch 9/10, Loss: 1.7891666094462078
Epoch 10/10, Loss: 1.6405238310496013
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.18 0.82 0.30 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.35 0.58 0.44 12
accuracy 0.23 70
macro avg 0.71 0.28 0.15 70
weighted avg 0.76 0.23 0.12 70
Training vgg with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 18176.781744996708
Epoch 2/10, Loss: 4.203017579184638
Epoch 3/10, Loss: 1.7165902588102553
Epoch 4/10, Loss: 1.8659958706961737
Epoch 5/10, Loss: 2.768445465299818
Epoch 6/10, Loss: 2.112853659523858
Epoch 7/10, Loss: 1.605770813094245
Epoch 8/10, Loss: 1.682369099722968
Epoch 9/10, Loss: 1.606081657939487
Epoch 10/10, Loss: 1.5957703590393066
Accuracy: 25.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.24 0.45 0.31 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.21 0.64 0.32 11
Vehicles 0.38 0.50 0.43 12
accuracy 0.26 70
macro avg 0.57 0.32 0.21 70
weighted avg 0.65 0.26 0.17 70
Training vgg with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 11513.051915513144
Epoch 2/10, Loss: 4.148666501045227
Epoch 3/10, Loss: 8.332034177250332
Epoch 4/10, Loss: 1.7981901963551838
Epoch 5/10, Loss: 1.6063409911261664
Epoch 6/10, Loss: 1.5962801112069025
Epoch 7/10, Loss: 1.5796233150694106
Epoch 8/10, Loss: 1.6120851304796007
Epoch 9/10, Loss: 2.0797599024242825
Epoch 10/10, Loss: 1.6145715051227145
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.8525073528289795
Epoch 2/10, Loss: 1.6049657821655274
Epoch 3/10, Loss: 1.555950117111206
Epoch 4/10, Loss: 1.5013535976409913
Epoch 5/10, Loss: 1.4901612281799317
Epoch 6/10, Loss: 1.295291781425476
Epoch 7/10, Loss: 1.1477572917938232
Epoch 8/10, Loss: 0.9683172345161438
Epoch 9/10, Loss: 0.9720304012298584
Epoch 10/10, Loss: 0.7620607376098633
Accuracy: 57.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.78 0.64 0.70 11
Mines 0.60 0.32 0.41 19
Pipes 0.58 0.65 0.61 17
Rockets 0.35 0.73 0.47 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.57 70
macro avg 0.64 0.60 0.59 70
weighted avg 0.63 0.57 0.58 70
Training vgg with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6417192459106444
Epoch 2/10, Loss: 1.5602328538894654
Epoch 3/10, Loss: 1.8259039163589477
Epoch 4/10, Loss: 1.6228607416152954
Epoch 5/10, Loss: 1.6040877103805542
Epoch 6/10, Loss: 1.6132249116897583
Epoch 7/10, Loss: 1.5824184179306031
Epoch 8/10, Loss: 1.7000040769577027
Epoch 9/10, Loss: 1.6090306043624878
Epoch 10/10, Loss: 1.5952816963195802
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.30 12
accuracy 0.17 70
macro avg 0.63 0.20 0.06 70
weighted avg 0.70 0.17 0.05 70
Training vgg with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6564398288726807
Epoch 2/10, Loss: 1.6815492868423463
Epoch 3/10, Loss: 1.6442558765411377
Epoch 4/10, Loss: 1.6145828008651733
Epoch 5/10, Loss: 1.5672864675521851
Epoch 6/10, Loss: 1.4339503049850464
Epoch 7/10, Loss: 1.5919858932495117
Epoch 8/10, Loss: 1.4563129186630248
Epoch 9/10, Loss: 1.454703426361084
Epoch 10/10, Loss: 1.3922101497650146
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.23 0.64 0.33 11
Vehicles 0.23 0.75 0.35 12
accuracy 0.23 70
macro avg 0.69 0.28 0.14 70
weighted avg 0.75 0.23 0.11 70
Training vgg with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.8715835332870483
Epoch 2/10, Loss: 1.6483039617538453
Epoch 3/10, Loss: 1.594318151473999
Epoch 4/10, Loss: 1.5859867811203003
Epoch 5/10, Loss: 1.5702788829803467
Epoch 6/10, Loss: 2.026367521286011
Epoch 7/10, Loss: 1.5880481243133544
Epoch 8/10, Loss: 1.5107162952423097
Epoch 9/10, Loss: 1.6084316730499268
Epoch 10/10, Loss: 1.5115361213684082
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.24 0.73 0.36 11
Mines 0.50 0.11 0.17 19
Pipes 1.00 0.00 0.00 17
Rockets 0.21 0.55 0.31 11
Vehicles 0.00 0.00 0.00 12
accuracy 0.23 70
macro avg 0.39 0.28 0.17 70
weighted avg 0.45 0.23 0.15 70
Training vgg with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7039016246795655
Epoch 2/10, Loss: 1.5989717960357666
Epoch 3/10, Loss: 1.5894911766052247
Epoch 4/10, Loss: 1.627537441253662
Epoch 5/10, Loss: 1.6414628982543946
Epoch 6/10, Loss: 1.5796934604644775
Epoch 7/10, Loss: 1.3537571430206299
Epoch 8/10, Loss: 1.1949524879455566
Epoch 9/10, Loss: 1.3125123262405396
Epoch 10/10, Loss: 1.1509268045425416
Accuracy: 52.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.46 0.55 0.50 11
Mines 0.64 0.47 0.55 19
Pipes 0.70 0.41 0.52 17
Rockets 0.28 0.45 0.34 11
Vehicles 0.67 0.83 0.74 12
accuracy 0.53 70
macro avg 0.55 0.54 0.53 70
weighted avg 0.57 0.53 0.53 70
Training vgg with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.8042707920074463
Epoch 2/10, Loss: 1.6004016160964967
Epoch 3/10, Loss: 1.668299674987793
Epoch 4/10, Loss: 1.560273289680481
Epoch 5/10, Loss: 1.4103358268737793
Epoch 6/10, Loss: 1.4833557605743408
Epoch 7/10, Loss: 1.2788233876228332
Epoch 8/10, Loss: 1.3337772846221925
Epoch 9/10, Loss: 1.143914246559143
Epoch 10/10, Loss: 1.018515980243683
Accuracy: 42.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.43 0.55 0.48 11
Mines 1.00 0.11 0.19 19
Pipes 0.55 0.35 0.43 17
Rockets 0.33 0.64 0.44 11
Vehicles 0.41 0.75 0.53 12
accuracy 0.43 70
macro avg 0.54 0.48 0.41 70
weighted avg 0.59 0.43 0.39 70
Training vgg with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.7468006134033203
Epoch 2/10, Loss: 1.6410756826400756
Epoch 3/10, Loss: 1.5397707939147949
Epoch 4/10, Loss: 1.603674292564392
Epoch 5/10, Loss: 1.4190776109695435
Epoch 6/10, Loss: 1.596405029296875
Epoch 7/10, Loss: 1.458656644821167
Epoch 8/10, Loss: 1.3778732538223266
Epoch 9/10, Loss: 1.280079197883606
Epoch 10/10, Loss: 1.178332471847534
Accuracy: 34.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.32 0.73 0.44 11
Mines 0.44 0.42 0.43 19
Pipes 1.00 0.00 0.00 17
Rockets 0.27 0.64 0.38 11
Vehicles 1.00 0.08 0.15 12
accuracy 0.34 70
macro avg 0.61 0.37 0.28 70
weighted avg 0.63 0.34 0.27 70
Training vgg with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.773143482208252
Epoch 2/10, Loss: 1.6570717811584472
Epoch 3/10, Loss: 1.5953577518463136
Epoch 4/10, Loss: 1.6050827503204346
Epoch 5/10, Loss: 1.564593291282654
Epoch 6/10, Loss: 1.479384469985962
Epoch 7/10, Loss: 1.4420658349990845
Epoch 8/10, Loss: 1.3505150079727173
Epoch 9/10, Loss: 1.3845108032226563
Epoch 10/10, Loss: 1.4298341274261475
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.29 0.45 0.36 11
Mines 0.50 0.11 0.17 19
Pipes 1.00 0.00 0.00 17
Rockets 0.30 0.27 0.29 11
Vehicles 0.23 0.75 0.35 12
accuracy 0.27 70
macro avg 0.46 0.32 0.23 70
weighted avg 0.51 0.27 0.21 70
Training vgg with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6381339073181151
Epoch 2/10, Loss: 1.691865611076355
Epoch 3/10, Loss: 1.594825029373169
Epoch 4/10, Loss: 1.5750099897384644
Epoch 5/10, Loss: 1.462172794342041
Epoch 6/10, Loss: 1.3794710159301757
Epoch 7/10, Loss: 1.2966441869735719
Epoch 8/10, Loss: 1.2868231773376464
Epoch 9/10, Loss: 0.9651436686515809
Epoch 10/10, Loss: 0.9232282519340516
Accuracy: 54.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.18 0.27 11
Mines 0.55 0.58 0.56 19
Pipes 0.61 0.82 0.70 17
Rockets 0.29 0.36 0.32 11
Vehicles 0.78 0.58 0.67 12
accuracy 0.54 70
macro avg 0.54 0.51 0.50 70
weighted avg 0.55 0.54 0.53 70
Training vgg with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6849883556365968
Epoch 2/10, Loss: 1.536073136329651
Epoch 3/10, Loss: 1.4474420070648193
Epoch 4/10, Loss: 1.36164231300354
Epoch 5/10, Loss: 1.2310393095016479
Epoch 6/10, Loss: 1.1111961483955384
Epoch 7/10, Loss: 0.9136436820030213
Epoch 8/10, Loss: 0.8154866814613342
Epoch 9/10, Loss: 0.6974999785423279
Epoch 10/10, Loss: 0.5555333495140076
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 0.78 0.74 0.76 19
Pipes 1.00 0.82 0.90 17
Rockets 0.89 0.73 0.80 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.81 70
macro avg 0.82 0.82 0.81 70
weighted avg 0.83 0.81 0.82 70
Training vgg with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.679495882987976
Epoch 2/10, Loss: 1.571409010887146
Epoch 3/10, Loss: 1.5016062021255494
Epoch 4/10, Loss: 1.3751184940338135
Epoch 5/10, Loss: 1.269765281677246
Epoch 6/10, Loss: 1.1720636129379272
Epoch 7/10, Loss: 1.0039470553398133
Epoch 8/10, Loss: 0.8699603080749512
Epoch 9/10, Loss: 0.6834193468093872
Epoch 10/10, Loss: 0.6168529748916626
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.65 1.00 0.79 11
Mines 0.88 0.79 0.83 19
Pipes 1.00 0.88 0.94 17
Rockets 0.73 0.73 0.73 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.83 70
macro avg 0.83 0.83 0.82 70
weighted avg 0.85 0.83 0.83 70
Training vgg with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.755109143257141
Epoch 2/10, Loss: 1.595996046066284
Epoch 3/10, Loss: 1.4608608484268188
Epoch 4/10, Loss: 1.3965209245681762
Epoch 5/10, Loss: 1.2844131469726563
Epoch 6/10, Loss: 1.2241896152496339
Epoch 7/10, Loss: 1.1472872018814086
Epoch 8/10, Loss: 0.9997863292694091
Epoch 9/10, Loss: 0.890610671043396
Epoch 10/10, Loss: 0.7416346430778503
Accuracy: 74.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.65 1.00 0.79 11
Mines 0.77 0.53 0.62 19
Pipes 0.94 0.88 0.91 17
Rockets 0.88 0.64 0.74 11
Vehicles 0.56 0.75 0.64 12
accuracy 0.74 70
macro avg 0.76 0.76 0.74 70
weighted avg 0.77 0.74 0.74 70
Training vgg with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6285175323486327
Epoch 2/10, Loss: 1.5251531600952148
Epoch 3/10, Loss: 1.4514522075653076
Epoch 4/10, Loss: 1.3342235326766967
Epoch 5/10, Loss: 1.1761977672576904
Epoch 6/10, Loss: 1.0437570929527282
Epoch 7/10, Loss: 0.82755446434021
Epoch 8/10, Loss: 0.6956390738487244
Epoch 9/10, Loss: 0.5719066143035889
Epoch 10/10, Loss: 0.45165082812309265
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 0.75 0.79 0.77 19
Pipes 1.00 0.82 0.90 17
Rockets 0.73 0.73 0.73 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.80 70
macro avg 0.81 0.80 0.80 70
weighted avg 0.82 0.80 0.80 70
Training vgg with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6622301816940308
Epoch 2/10, Loss: 1.6230654954910277
Epoch 3/10, Loss: 1.4366631746292113
Epoch 4/10, Loss: 1.3390491485595704
Epoch 5/10, Loss: 1.2539413452148438
Epoch 6/10, Loss: 1.1322071552276611
Epoch 7/10, Loss: 0.9947212100028991
Epoch 8/10, Loss: 0.8106290340423584
Epoch 9/10, Loss: 0.737639045715332
Epoch 10/10, Loss: 0.6464431405067443
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.65 1.00 0.79 11
Mines 0.84 0.84 0.84 19
Pipes 1.00 0.82 0.90 17
Rockets 0.73 0.73 0.73 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.81 70
macro avg 0.82 0.81 0.80 70
weighted avg 0.84 0.81 0.82 70
Training vgg with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.7102307558059693
Epoch 2/10, Loss: 1.6364579677581788
Epoch 3/10, Loss: 1.5545337915420532
Epoch 4/10, Loss: 1.4001958608627318
Epoch 5/10, Loss: 1.2871940851211547
Epoch 6/10, Loss: 1.2177799224853516
Epoch 7/10, Loss: 1.0967552542686463
Epoch 8/10, Loss: 1.0410418272018434
Epoch 9/10, Loss: 0.8928029537200928
Epoch 10/10, Loss: 0.7335364937782287
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.85 0.58 0.69 19
Pipes 0.93 0.82 0.88 17
Rockets 0.78 0.64 0.70 11
Vehicles 0.55 0.92 0.69 12
accuracy 0.76 70
macro avg 0.78 0.77 0.76 70
weighted avg 0.79 0.76 0.76 70
Training vgg with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6263129234313964
Epoch 2/10, Loss: 1.5640941619873048
Epoch 3/10, Loss: 1.453252911567688
Epoch 4/10, Loss: 1.299035120010376
Epoch 5/10, Loss: 1.159316897392273
Epoch 6/10, Loss: 1.0322484254837037
Epoch 7/10, Loss: 0.9333159923553467
Epoch 8/10, Loss: 0.8084739446640015
Epoch 9/10, Loss: 0.6414490580558777
Epoch 10/10, Loss: 0.5467397034168243
Accuracy: 78.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 0.79 0.79 0.79 19
Pipes 1.00 0.76 0.87 17
Rockets 0.70 0.64 0.67 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.79 70
macro avg 0.79 0.79 0.78 70
weighted avg 0.80 0.79 0.79 70
Training vgg with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6640917778015136
Epoch 2/10, Loss: 1.5907882928848267
Epoch 3/10, Loss: 1.427307367324829
Epoch 4/10, Loss: 1.397084617614746
Epoch 5/10, Loss: 1.272442674636841
Epoch 6/10, Loss: 1.1637845039367676
Epoch 7/10, Loss: 1.018549871444702
Epoch 8/10, Loss: 0.9151194095611572
Epoch 9/10, Loss: 0.7770587682724
Epoch 10/10, Loss: 0.6800689458847046
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.60 0.82 0.69 11
Mines 0.64 0.74 0.68 19
Pipes 0.93 0.76 0.84 17
Rockets 0.67 0.55 0.60 11
Vehicles 0.70 0.58 0.64 12
accuracy 0.70 70
macro avg 0.71 0.69 0.69 70
weighted avg 0.72 0.70 0.70 70
Training vgg with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.7025739669799804
Epoch 2/10, Loss: 1.6127503156661986
Epoch 3/10, Loss: 1.509557557106018
Epoch 4/10, Loss: 1.3861327171325684
Epoch 5/10, Loss: 1.2776071548461914
Epoch 6/10, Loss: 1.257564330101013
Epoch 7/10, Loss: 1.0739696741104126
Epoch 8/10, Loss: 1.0103334784507751
Epoch 9/10, Loss: 0.8431547045707702
Epoch 10/10, Loss: 0.744294273853302
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.82 0.72 11
Mines 0.72 0.68 0.70 19
Pipes 0.88 0.82 0.85 17
Rockets 0.50 0.45 0.48 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.71 70
macro avg 0.70 0.71 0.70 70
weighted avg 0.72 0.71 0.71 70
Training vgg with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 39314.63781044483
Epoch 2/10, Loss: 89.7999655008316
Epoch 3/10, Loss: 5.543510150909424
Epoch 4/10, Loss: 2.1040927171707153
Epoch 5/10, Loss: 1.674718165397644
Epoch 6/10, Loss: 2.7115574359893797
Epoch 7/10, Loss: 1.597165012359619
Epoch 8/10, Loss: 1.5851209878921508
Epoch 9/10, Loss: 1.609391951560974
Epoch 10/10, Loss: 1.6126499891281127
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 27723.464806985856
Epoch 2/10, Loss: 3.6477128505706786
Epoch 3/10, Loss: 1.753441333770752
Epoch 4/10, Loss: 1.8464706182479858
Epoch 5/10, Loss: 1.7069947957992553
Epoch 6/10, Loss: 1.6296997785568237
Epoch 7/10, Loss: 1.5867652654647828
Epoch 8/10, Loss: 1.8254354000091553
Epoch 9/10, Loss: 1.7348535060882568
Epoch 10/10, Loss: 1.6661692619323731
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.16 1.00 0.27 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.16 70
macro avg 0.83 0.20 0.05 70
weighted avg 0.87 0.16 0.04 70
Training vgg with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 4201.986796569824
Epoch 2/10, Loss: 3.1233475685119627
Epoch 3/10, Loss: 1.8260760307312012
Epoch 4/10, Loss: 1.7704455137252808
Epoch 5/10, Loss: 1.739628219604492
Epoch 6/10, Loss: 1.6052682399749756
Epoch 7/10, Loss: 1.651440477371216
Epoch 8/10, Loss: 1.5988948583602904
Epoch 9/10, Loss: 1.6055893659591676
Epoch 10/10, Loss: 1.6004143238067627
Accuracy: 21.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.19 0.91 0.31 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.29 0.42 0.34 12
accuracy 0.21 70
macro avg 0.70 0.27 0.13 70
weighted avg 0.75 0.21 0.11 70
Training vgg with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 35063.90391185284
Epoch 2/10, Loss: 1.9110910177230835
Epoch 3/10, Loss: 1.953963303565979
Epoch 4/10, Loss: 1.7917446613311767
Epoch 5/10, Loss: 2.176320743560791
Epoch 6/10, Loss: 1.633564281463623
Epoch 7/10, Loss: 1.8274413347244263
Epoch 8/10, Loss: 1.775822353363037
Epoch 9/10, Loss: 1.58278968334198
Epoch 10/10, Loss: 1.7306080341339112
Accuracy: 21.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.25 0.68 0.36 19
Pipes 1.00 0.00 0.00 17
Rockets 0.12 0.18 0.14 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.21 70
macro avg 0.67 0.17 0.10 70
weighted avg 0.66 0.21 0.12 70
Training vgg with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 12334.414453554153
Epoch 2/10, Loss: 9.767044830322266
Epoch 3/10, Loss: 7.106862211227417
Epoch 4/10, Loss: 1.755237865447998
Epoch 5/10, Loss: 1.61668963432312
Epoch 6/10, Loss: 1.5972941875457765
Epoch 7/10, Loss: 1.6000550985336304
Epoch 8/10, Loss: 1.6078855276107789
Epoch 9/10, Loss: 1.6121466398239135
Epoch 10/10, Loss: 1.6010303497314453
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 74281.89723637103
Epoch 2/10, Loss: 6.028094863891601
Epoch 3/10, Loss: 3.4136422634124757
Epoch 4/10, Loss: 1.6277648210525513
Epoch 5/10, Loss: 1.6105790376663207
Epoch 6/10, Loss: 1.6003494024276734
Epoch 7/10, Loss: 1.6140952587127686
Epoch 8/10, Loss: 1.6640914678573608
Epoch 9/10, Loss: 1.6143165588378907
Epoch 10/10, Loss: 1.5928068161010742
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 74026.57080430984
Epoch 2/10, Loss: 17.15080122947693
Epoch 3/10, Loss: 1.6444536685943603
Epoch 4/10, Loss: 2.0273465633392336
Epoch 5/10, Loss: 1.6277834892272949
Epoch 6/10, Loss: 1.7327149629592895
Epoch 7/10, Loss: 1.6591815948486328
Epoch 8/10, Loss: 1.6834177732467652
Epoch 9/10, Loss: 1.626923394203186
Epoch 10/10, Loss: 1.6307858228683472
Accuracy: 17.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.17 1.00 0.29 12
accuracy 0.17 70
macro avg 0.83 0.20 0.06 70
weighted avg 0.86 0.17 0.05 70
Training vgg with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 100413.75132853985
Epoch 2/10, Loss: 7.097348427772522
Epoch 3/10, Loss: 2.4645736694335936
Epoch 4/10, Loss: 1.9068748474121093
Epoch 5/10, Loss: 1.6158340692520141
Epoch 6/10, Loss: 1.6979585647583009
Epoch 7/10, Loss: 1.615902042388916
Epoch 8/10, Loss: 1.7636204004287719
Epoch 9/10, Loss: 1.8103537559509277
Epoch 10/10, Loss: 1.6009934663772583
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.27 1.00 0.43 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.27 70
macro avg 0.85 0.20 0.09 70
weighted avg 0.80 0.27 0.12 70
Training vgg with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 46896.08756344319
Epoch 2/10, Loss: 11.23885145187378
Epoch 3/10, Loss: 2.1081950187683107
Epoch 4/10, Loss: 1.6854470252990723
Epoch 5/10, Loss: 1.7646026611328125
Epoch 6/10, Loss: 1.805299949645996
Epoch 7/10, Loss: 1.6694497108459472
Epoch 8/10, Loss: 1.6386727809906005
Epoch 9/10, Loss: 1.681697630882263
Epoch 10/10, Loss: 2.412265586853027
Accuracy: 15.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.16 1.00 0.27 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.16 70
macro avg 0.83 0.20 0.05 70
weighted avg 0.87 0.16 0.04 70
Training vgg with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.36748688088523
Epoch 2/10, Loss: 0.7522323512368732
Epoch 3/10, Loss: 0.3271905094799068
Epoch 4/10, Loss: 0.24479086957742563
Epoch 5/10, Loss: 0.21755053672111696
Epoch 6/10, Loss: 0.07161782087415405
Epoch 7/10, Loss: 0.025858340607050598
Epoch 8/10, Loss: 0.003838586183216345
Epoch 9/10, Loss: 0.009590439022253526
Epoch 10/10, Loss: 0.0005289525204009098
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.82 0.95 0.88 19
Pipes 0.94 0.88 0.91 17
Rockets 0.90 0.82 0.86 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.87 70
macro avg 0.88 0.86 0.87 70
weighted avg 0.88 0.87 0.87 70
Training vgg with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.5596932371457417
Epoch 2/10, Loss: 1.0559589034981198
Epoch 3/10, Loss: 0.5003957413136959
Epoch 4/10, Loss: 0.28499866897861165
Epoch 5/10, Loss: 0.1461897221290403
Epoch 6/10, Loss: 0.17852977585668364
Epoch 7/10, Loss: 0.10045245955956893
Epoch 8/10, Loss: 0.2756586361728195
Epoch 9/10, Loss: 0.1241014293498463
Epoch 10/10, Loss: 0.08042530675043559
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.75 0.79 0.77 19
Pipes 1.00 0.82 0.90 17
Rockets 0.78 0.64 0.70 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.81 70
macro avg 0.82 0.82 0.81 70
weighted avg 0.83 0.81 0.81 70
Training vgg with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.5998919473754034
Epoch 2/10, Loss: 1.2452920807732477
Epoch 3/10, Loss: 0.857900126112832
Epoch 4/10, Loss: 0.5877744489245944
Epoch 5/10, Loss: 0.4226199835538864
Epoch 6/10, Loss: 0.1882876924452527
Epoch 7/10, Loss: 0.22844465651238957
Epoch 8/10, Loss: 0.1350838272418413
Epoch 9/10, Loss: 0.06008207951931076
Epoch 10/10, Loss: 0.05987655238196668
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.94 0.89 0.92 19
Pipes 1.00 0.82 0.90 17
Rockets 0.79 1.00 0.88 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.89 70
macro avg 0.88 0.89 0.88 70
weighted avg 0.90 0.89 0.89 70
Training vgg with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.310019314289093
Epoch 2/10, Loss: 0.6665227835377058
Epoch 3/10, Loss: 0.3494684584438801
Epoch 4/10, Loss: 0.09850464223159684
Epoch 5/10, Loss: 0.08745698307737054
Epoch 6/10, Loss: 0.27405354225387174
Epoch 7/10, Loss: 0.08159268996678293
Epoch 8/10, Loss: 0.036215116183787664
Epoch 9/10, Loss: 0.03965831560748888
Epoch 10/10, Loss: 0.005942591980177288
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.94 0.84 0.89 19
Pipes 0.89 0.94 0.91 17
Rockets 0.90 0.82 0.86 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.87 70
macro avg 0.87 0.87 0.87 70
weighted avg 0.88 0.87 0.87 70
Training vgg with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5502777298291524
Epoch 2/10, Loss: 0.9615585621860292
Epoch 3/10, Loss: 0.48938450548383927
Epoch 4/10, Loss: 0.17581189897221824
Epoch 5/10, Loss: 0.1969953768550315
Epoch 6/10, Loss: 0.2536568756008314
Epoch 7/10, Loss: 0.18449037464516652
Epoch 8/10, Loss: 0.2077027237456706
Epoch 9/10, Loss: 0.09393027833074383
Epoch 10/10, Loss: 0.06837754034010383
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.70 0.64 0.67 11
Mines 0.86 0.95 0.90 19
Pipes 0.89 1.00 0.94 17
Rockets 0.70 0.64 0.67 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.83 70
macro avg 0.81 0.79 0.80 70
weighted avg 0.82 0.83 0.82 70
Training vgg with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6293962531619601
Epoch 2/10, Loss: 1.278076022863388
Epoch 3/10, Loss: 0.9253937717941072
Epoch 4/10, Loss: 0.5203841303785642
Epoch 5/10, Loss: 0.45273276327902245
Epoch 6/10, Loss: 0.18496032345056948
Epoch 7/10, Loss: 0.2692478166686164
Epoch 8/10, Loss: 0.13214318283523122
Epoch 9/10, Loss: 0.06306880887132138
Epoch 10/10, Loss: 0.04811557402313661
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.58 1.00 0.73 11
Mines 0.94 0.84 0.89 19
Pipes 1.00 0.88 0.94 17
Rockets 0.89 0.73 0.80 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.84 70
macro avg 0.86 0.84 0.84 70
weighted avg 0.88 0.84 0.85 70
Training vgg with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.3961081703503926
Epoch 2/10, Loss: 0.7969230115413666
Epoch 3/10, Loss: 0.46794662314156693
Epoch 4/10, Loss: 0.2166307066153321
Epoch 5/10, Loss: 0.09391825622878969
Epoch 6/10, Loss: 0.21939480837075584
Epoch 7/10, Loss: 0.16843240594284403
Epoch 8/10, Loss: 0.09060318888643654
Epoch 9/10, Loss: 0.06822826221307171
Epoch 10/10, Loss: 0.03491233025690437
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.83 0.79 0.81 19
Pipes 1.00 0.88 0.94 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.87 70
macro avg 0.88 0.88 0.87 70
weighted avg 0.89 0.87 0.87 70
Training vgg with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5345892310142517
Epoch 2/10, Loss: 0.9101674656073252
Epoch 3/10, Loss: 0.6600829213857651
Epoch 4/10, Loss: 0.23066081115555587
Epoch 5/10, Loss: 0.15165245338963965
Epoch 6/10, Loss: 0.15073465158800697
Epoch 7/10, Loss: 0.09015513092486395
Epoch 8/10, Loss: 0.07357334544778699
Epoch 9/10, Loss: 0.13484123993354538
Epoch 10/10, Loss: 0.31739599257707596
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.55 1.00 0.71 11
Mines 0.86 0.95 0.90 19
Pipes 0.89 0.94 0.91 17
Rockets 1.00 0.09 0.17 11
Vehicles 0.80 0.67 0.73 12
accuracy 0.77 70
macro avg 0.82 0.73 0.68 70
weighted avg 0.83 0.77 0.73 70
Training vgg with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5771433578597174
Epoch 2/10, Loss: 1.1869900557729933
Epoch 3/10, Loss: 0.6917643936143981
Epoch 4/10, Loss: 0.4411260642939144
Epoch 5/10, Loss: 0.29250088127122986
Epoch 6/10, Loss: 0.13976827998542124
Epoch 7/10, Loss: 0.10718047947415875
Epoch 8/10, Loss: 0.06046160139117597
Epoch 9/10, Loss: 0.037525941286326595
Epoch 10/10, Loss: 0.2797259491829512
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.73 0.84 11
Mines 0.73 0.84 0.78 19
Pipes 1.00 0.76 0.87 17
Rockets 0.62 0.91 0.74 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.80 70
macro avg 0.83 0.80 0.80 70
weighted avg 0.84 0.80 0.81 70
Training vgg with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.679378072420756
Epoch 2/10, Loss: 1.5501051677597895
Epoch 3/10, Loss: 1.4113810062408447
Epoch 4/10, Loss: 1.309545675913493
Epoch 5/10, Loss: 1.212841014067332
Epoch 6/10, Loss: 1.0819067226515875
Epoch 7/10, Loss: 0.920562164651023
Epoch 8/10, Loss: 0.7964692811171213
Epoch 9/10, Loss: 0.6605862494972017
Epoch 10/10, Loss: 0.5415923148393631
Accuracy: 78.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 0.88 0.79 0.83 19
Pipes 1.00 0.82 0.90 17
Rockets 0.62 0.73 0.67 11
Vehicles 0.70 0.58 0.64 12
accuracy 0.79 70
macro avg 0.78 0.78 0.77 70
weighted avg 0.81 0.79 0.79 70
Training vgg with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.692513797018263
Epoch 2/10, Loss: 1.566187732749515
Epoch 3/10, Loss: 1.459010296397739
Epoch 4/10, Loss: 1.3264939453866746
Epoch 5/10, Loss: 1.265493820110957
Epoch 6/10, Loss: 1.1442425184779697
Epoch 7/10, Loss: 1.0061915351284876
Epoch 8/10, Loss: 0.9350780778461032
Epoch 9/10, Loss: 0.778607640001509
Epoch 10/10, Loss: 0.6556850473086039
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.80 0.63 0.71 19
Pipes 1.00 0.88 0.94 17
Rockets 0.67 0.55 0.60 11
Vehicles 0.62 0.83 0.71 12
accuracy 0.77 70
macro avg 0.76 0.78 0.76 70
weighted avg 0.79 0.77 0.77 70
Training vgg with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.7447891301578946
Epoch 2/10, Loss: 1.5953762133916218
Epoch 3/10, Loss: 1.4691619873046875
Epoch 4/10, Loss: 1.4009302523401048
Epoch 5/10, Loss: 1.3437789413664076
Epoch 6/10, Loss: 1.196518447664049
Epoch 7/10, Loss: 1.1412363880210452
Epoch 8/10, Loss: 1.0281362301773496
Epoch 9/10, Loss: 0.9487617313861847
Epoch 10/10, Loss: 0.7911145074499978
Accuracy: 74.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.61 1.00 0.76 11
Mines 0.87 0.68 0.76 19
Pipes 0.93 0.82 0.88 17
Rockets 0.60 0.55 0.57 11
Vehicles 0.67 0.67 0.67 12
accuracy 0.74 70
macro avg 0.74 0.74 0.73 70
weighted avg 0.77 0.74 0.74 70
Training vgg with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.7163866029845343
Epoch 2/10, Loss: 1.6051188442442152
Epoch 3/10, Loss: 1.4382852050993178
Epoch 4/10, Loss: 1.3321032524108887
Epoch 5/10, Loss: 1.2228307326634724
Epoch 6/10, Loss: 1.1462780601448483
Epoch 7/10, Loss: 0.9910891950130463
Epoch 8/10, Loss: 0.8537752893235948
Epoch 9/10, Loss: 0.7565299106968774
Epoch 10/10, Loss: 0.6448333097828759
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.59 0.91 0.71 11
Mines 0.84 0.84 0.84 19
Pipes 1.00 0.88 0.94 17
Rockets 0.50 0.55 0.52 11
Vehicles 0.86 0.50 0.63 12
accuracy 0.76 70
macro avg 0.76 0.74 0.73 70
weighted avg 0.79 0.76 0.76 70
Training vgg with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6983828213479784
Epoch 2/10, Loss: 1.5304628544383578
Epoch 3/10, Loss: 1.4379598961936102
Epoch 4/10, Loss: 1.3421412971284654
Epoch 5/10, Loss: 1.2597245110405817
Epoch 6/10, Loss: 1.1044820149739583
Epoch 7/10, Loss: 0.9664153224892087
Epoch 8/10, Loss: 0.8846498396661546
Epoch 9/10, Loss: 0.7653549777136909
Epoch 10/10, Loss: 0.6498047179645963
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.55 1.00 0.71 11
Mines 0.87 0.68 0.76 19
Pipes 1.00 0.76 0.87 17
Rockets 0.64 0.64 0.64 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.76 70
macro avg 0.77 0.77 0.75 70
weighted avg 0.80 0.76 0.76 70
Training vgg with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.671679503387875
Epoch 2/10, Loss: 1.5528939300113254
Epoch 3/10, Loss: 1.4738293422593012
Epoch 4/10, Loss: 1.3476711445384555
Epoch 5/10, Loss: 1.2666418486171298
Epoch 6/10, Loss: 1.1960789925522275
Epoch 7/10, Loss: 1.0514278180069394
Epoch 8/10, Loss: 0.9401011102729373
Epoch 9/10, Loss: 0.8693622152010599
Epoch 10/10, Loss: 0.7164232532183329
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.75 0.79 0.77 19
Pipes 1.00 0.82 0.90 17
Rockets 0.67 0.55 0.60 11
Vehicles 0.69 0.75 0.72 12
accuracy 0.77 70
macro avg 0.76 0.76 0.76 70
weighted avg 0.78 0.77 0.77 70
Training vgg with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.626589960522122
Epoch 2/10, Loss: 1.4826436506377325
Epoch 3/10, Loss: 1.382905642191569
Epoch 4/10, Loss: 1.229523393842909
Epoch 5/10, Loss: 1.1480246053801642
Epoch 6/10, Loss: 0.9574335482385423
Epoch 7/10, Loss: 0.8674271586868498
Epoch 8/10, Loss: 0.7442721890078651
Epoch 9/10, Loss: 0.6009508288568921
Epoch 10/10, Loss: 0.5327874256504906
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.87 0.68 0.76 19
Pipes 1.00 0.94 0.97 17
Rockets 0.73 0.73 0.73 11
Vehicles 0.69 0.75 0.72 12
accuracy 0.80 70
macro avg 0.79 0.80 0.79 70
weighted avg 0.82 0.80 0.80 70
Training vgg with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6550455821885004
Epoch 2/10, Loss: 1.571477969487508
Epoch 3/10, Loss: 1.4118534922599792
Epoch 4/10, Loss: 1.349501325024499
Epoch 5/10, Loss: 1.2529022031360202
Epoch 6/10, Loss: 1.1135834952195485
Epoch 7/10, Loss: 1.0411779946751065
Epoch 8/10, Loss: 0.89219355252054
Epoch 9/10, Loss: 0.8085470067130195
Epoch 10/10, Loss: 0.704547729757097
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.87 0.68 0.76 19
Pipes 1.00 0.82 0.90 17
Rockets 0.80 0.73 0.76 11
Vehicles 0.53 0.75 0.62 12
accuracy 0.77 70
macro avg 0.78 0.78 0.77 70
weighted avg 0.81 0.77 0.78 70
Training vgg with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.7042273150549994
Epoch 2/10, Loss: 1.6021063129107158
Epoch 3/10, Loss: 1.4771764477094014
Epoch 4/10, Loss: 1.3870760599772136
Epoch 5/10, Loss: 1.2226807806226943
Epoch 6/10, Loss: 1.1951613161298964
Epoch 7/10, Loss: 1.1060062812434301
Epoch 8/10, Loss: 0.9323969069454405
Epoch 9/10, Loss: 0.8112156291802725
Epoch 10/10, Loss: 0.7512983282407125
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.91 0.69 11
Mines 0.73 0.58 0.65 19
Pipes 1.00 0.76 0.87 17
Rockets 0.67 0.73 0.70 11
Vehicles 0.67 0.67 0.67 12
accuracy 0.71 70
macro avg 0.72 0.73 0.71 70
weighted avg 0.75 0.71 0.72 70
Training vgg with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.7563228673405118
Epoch 2/10, Loss: 1.7877497673034668
Epoch 3/10, Loss: 2.1147396432028875
Epoch 4/10, Loss: 1.5333203143543668
Epoch 5/10, Loss: 1.318540804915958
Epoch 6/10, Loss: 1.2450801995065477
Epoch 7/10, Loss: 1.421704928080241
Epoch 8/10, Loss: 1.2064786321587033
Epoch 9/10, Loss: 1.0915764835145738
Epoch 10/10, Loss: 1.2069065471490223
Accuracy: 35.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.55 0.55 0.55 11
Mines 0.31 0.53 0.39 19
Pipes 1.00 0.00 0.00 17
Rockets 0.19 0.36 0.25 11
Vehicles 0.83 0.42 0.56 12
accuracy 0.36 70
macro avg 0.58 0.37 0.35 70
weighted avg 0.59 0.36 0.33 70
Training vgg with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.7072082625495062
Epoch 2/10, Loss: 1.6253183947669134
Epoch 3/10, Loss: 1.5602177845107184
Epoch 4/10, Loss: 1.773398631148868
Epoch 5/10, Loss: 1.3988732761806912
Epoch 6/10, Loss: 1.1900829275449116
Epoch 7/10, Loss: 1.1102493206659954
Epoch 8/10, Loss: 0.8442451771762636
Epoch 9/10, Loss: 0.8325990074210696
Epoch 10/10, Loss: 0.5575118321511481
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.70 0.64 0.67 11
Mines 0.74 0.74 0.74 19
Pipes 0.83 0.59 0.69 17
Rockets 0.53 0.82 0.64 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.70 70
macro avg 0.71 0.71 0.70 70
weighted avg 0.72 0.70 0.70 70
Training vgg with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.7343084613482158
Epoch 2/10, Loss: 1.6032677226596408
Epoch 3/10, Loss: 1.6133556167284648
Epoch 4/10, Loss: 1.5933141907056172
Epoch 5/10, Loss: 1.4842292666435242
Epoch 6/10, Loss: 1.3808157973819308
Epoch 7/10, Loss: 1.2334501014815435
Epoch 8/10, Loss: 1.2426280147499509
Epoch 9/10, Loss: 1.19369841615359
Epoch 10/10, Loss: 1.0019515007734299
Accuracy: 48.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.53 0.73 0.62 11
Mines 0.50 0.32 0.39 19
Pipes 0.52 0.71 0.60 17
Rockets 0.00 0.00 0.00 11
Vehicles 0.42 0.67 0.52 12
accuracy 0.49 70
macro avg 0.40 0.48 0.42 70
weighted avg 0.42 0.49 0.44 70
Training vgg with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 2.3641043504079184
Epoch 2/10, Loss: 1.5932961503664653
Epoch 3/10, Loss: 1.5292938616540697
Epoch 4/10, Loss: 1.4119035138024225
Epoch 5/10, Loss: 1.2706264456113179
Epoch 6/10, Loss: 1.3104351527161069
Epoch 7/10, Loss: 1.2010131180286407
Epoch 8/10, Loss: 1.0349405043654971
Epoch 9/10, Loss: 0.9851436747445
Epoch 10/10, Loss: 0.7620702120992873
Accuracy: 34.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.27 0.55 0.36 11
Mines 0.27 0.16 0.20 19
Pipes 1.00 0.12 0.21 17
Rockets 0.20 0.27 0.23 11
Vehicles 0.50 0.83 0.62 12
accuracy 0.34 70
macro avg 0.45 0.39 0.33 70
weighted avg 0.48 0.34 0.31 70
Training vgg with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7291570504506428
Epoch 2/10, Loss: 1.6230873531765408
Epoch 3/10, Loss: 1.5782937937312655
Epoch 4/10, Loss: 1.7940813369221158
Epoch 5/10, Loss: 1.5988374948501587
Epoch 6/10, Loss: 1.4099518987867568
Epoch 7/10, Loss: 1.2060667640633054
Epoch 8/10, Loss: 1.0854034490055509
Epoch 9/10, Loss: 0.9082056250837114
Epoch 10/10, Loss: 0.6759800513585409
Accuracy: 50.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.78 0.64 0.70 11
Mines 0.41 0.37 0.39 19
Pipes 1.00 0.29 0.45 17
Rockets 0.50 0.55 0.52 11
Vehicles 0.37 0.83 0.51 12
accuracy 0.50 70
macro avg 0.61 0.54 0.52 70
weighted avg 0.62 0.50 0.50 70
Training vgg with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.797540373272366
Epoch 2/10, Loss: 1.6069654756122165
Epoch 3/10, Loss: 1.6358046995268927
Epoch 4/10, Loss: 1.414582696225908
Epoch 5/10, Loss: 1.264202078183492
Epoch 6/10, Loss: 1.0577566060754988
Epoch 7/10, Loss: 1.0417310727967157
Epoch 8/10, Loss: 0.9450808730390337
Epoch 9/10, Loss: 0.939369711611006
Epoch 10/10, Loss: 0.9055491222275628
Accuracy: 42.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.80 0.36 0.50 11
Mines 0.50 0.42 0.46 19
Pipes 1.00 0.00 0.00 17
Rockets 0.26 0.91 0.40 11
Vehicles 0.80 0.67 0.73 12
accuracy 0.43 70
macro avg 0.67 0.47 0.42 70
weighted avg 0.68 0.43 0.39 70
Training vgg with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 2.191856066385905
Epoch 2/10, Loss: 1.6328325867652893
Epoch 3/10, Loss: 1.6103233363893297
Epoch 4/10, Loss: 1.3630495336320665
Epoch 5/10, Loss: 1.3824264605840046
Epoch 6/10, Loss: 1.2105493876669142
Epoch 7/10, Loss: 1.082564052608278
Epoch 8/10, Loss: 1.1907851182752185
Epoch 9/10, Loss: 0.9685178183847003
Epoch 10/10, Loss: 0.7074006398518881
Accuracy: 62.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.45 0.59 11
Mines 0.65 0.79 0.71 19
Pipes 0.67 0.59 0.62 17
Rockets 0.36 0.45 0.40 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.63 70
macro avg 0.65 0.61 0.62 70
weighted avg 0.65 0.63 0.63 70
Training vgg with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7637417515118916
Epoch 2/10, Loss: 1.6004724502563477
Epoch 3/10, Loss: 1.730091392993927
Epoch 4/10, Loss: 1.3838482234213088
Epoch 5/10, Loss: 1.3187256819672055
Epoch 6/10, Loss: 1.4943863054116566
Epoch 7/10, Loss: 1.076581941710578
Epoch 8/10, Loss: 0.9817418621646034
Epoch 9/10, Loss: 1.0892787211471133
Epoch 10/10, Loss: 0.9176682598061032
Accuracy: 50.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.58 0.64 0.61 11
Mines 0.50 0.11 0.17 19
Pipes 0.56 0.59 0.57 17
Rockets 0.34 0.91 0.50 11
Vehicles 0.86 0.50 0.63 12
accuracy 0.50 70
macro avg 0.57 0.55 0.50 70
weighted avg 0.56 0.50 0.47 70
Training vgg with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6752621531486511
Epoch 2/10, Loss: 1.6285218530231051
Epoch 3/10, Loss: 1.4715416431427002
Epoch 4/10, Loss: 1.4790015949143305
Epoch 5/10, Loss: 1.2254355019993253
Epoch 6/10, Loss: 1.1211338076326582
Epoch 7/10, Loss: 1.0351820786794026
Epoch 8/10, Loss: 1.0455882284376357
Epoch 9/10, Loss: 0.8935260673364004
Epoch 10/10, Loss: 0.7479203417897224
Accuracy: 45.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.64 0.64 11
Mines 0.75 0.47 0.58 19
Pipes 1.00 0.00 0.00 17
Rockets 0.38 0.55 0.44 11
Vehicles 0.32 0.83 0.47 12
accuracy 0.46 70
macro avg 0.62 0.50 0.43 70
weighted avg 0.66 0.46 0.41 70
Training vgg with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.5696431928210788
Epoch 2/10, Loss: 1.080222725868225
Epoch 3/10, Loss: 0.560480147600174
Epoch 4/10, Loss: 0.26403116020891404
Epoch 5/10, Loss: 0.17035114454726377
Epoch 6/10, Loss: 0.10957481815583175
Epoch 7/10, Loss: 0.05800812287877003
Epoch 8/10, Loss: 0.031021407773045614
Epoch 9/10, Loss: 0.01440106221060786
Epoch 10/10, Loss: 0.07610657890108996
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 1.00 0.63 0.77 19
Pipes 0.94 0.94 0.94 17
Rockets 0.62 0.91 0.74 11
Vehicles 0.73 0.67 0.70 12
accuracy 0.80 70
macro avg 0.80 0.81 0.79 70
weighted avg 0.84 0.80 0.80 70
Training vgg with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.533732361263699
Epoch 2/10, Loss: 0.9813389910591973
Epoch 3/10, Loss: 0.46938204103045994
Epoch 4/10, Loss: 0.35254008571306866
Epoch 5/10, Loss: 0.1582817095849249
Epoch 6/10, Loss: 0.10668597375560138
Epoch 7/10, Loss: 0.0541594358575013
Epoch 8/10, Loss: 0.04055759359875487
Epoch 9/10, Loss: 0.007413958632645922
Epoch 10/10, Loss: 0.0006907532656946892
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.82 0.74 0.78 19
Pipes 1.00 0.82 0.90 17
Rockets 0.62 0.91 0.74 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.84 70
macro avg 0.86 0.86 0.85 70
weighted avg 0.87 0.84 0.85 70
Training vgg with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6266637908087835
Epoch 2/10, Loss: 1.3455255296495225
Epoch 3/10, Loss: 0.8539991014533572
Epoch 4/10, Loss: 0.4374980280796687
Epoch 5/10, Loss: 0.1755259550280041
Epoch 6/10, Loss: 0.2842649287647671
Epoch 7/10, Loss: 0.22097655799653795
Epoch 8/10, Loss: 0.05482723522517416
Epoch 9/10, Loss: 0.02081106994753807
Epoch 10/10, Loss: 0.004883598955024758
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.82 0.95 0.88 19
Pipes 1.00 0.82 0.90 17
Rockets 1.00 0.64 0.78 11
Vehicles 0.79 0.92 0.85 12
accuracy 0.87 70
macro avg 0.89 0.86 0.86 70
weighted avg 0.89 0.87 0.87 70
Training vgg with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.4511111312442355
Epoch 2/10, Loss: 0.6968183120091757
Epoch 3/10, Loss: 0.3830217983987596
Epoch 4/10, Loss: 0.28361248845855397
Epoch 5/10, Loss: 0.15829685206214586
Epoch 6/10, Loss: 0.08514083963301447
Epoch 7/10, Loss: 0.20358411760793793
Epoch 8/10, Loss: 0.07456276348481576
Epoch 9/10, Loss: 0.01947397981873817
Epoch 10/10, Loss: 0.0037025208196913204
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.87 0.68 0.76 19
Pipes 1.00 0.82 0.90 17
Rockets 0.67 0.91 0.77 11
Vehicles 0.92 0.92 0.92 12
accuracy 0.84 70
macro avg 0.85 0.87 0.85 70
weighted avg 0.86 0.84 0.84 70
Training vgg with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5393238994810317
Epoch 2/10, Loss: 0.9711797634760538
Epoch 3/10, Loss: 0.5140125552813212
Epoch 4/10, Loss: 0.34847787105374867
Epoch 5/10, Loss: 0.1679250126083692
Epoch 6/10, Loss: 0.07241010640023483
Epoch 7/10, Loss: 0.04860672499570581
Epoch 8/10, Loss: 0.026591126470723085
Epoch 9/10, Loss: 0.1403226069588628
Epoch 10/10, Loss: 0.1491241753101349
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.73 0.84 11
Mines 0.79 0.79 0.79 19
Pipes 1.00 0.88 0.94 17
Rockets 0.90 0.82 0.86 11
Vehicles 0.61 0.92 0.73 12
accuracy 0.83 70
macro avg 0.86 0.83 0.83 70
weighted avg 0.86 0.83 0.83 70
Training vgg with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6417370902167425
Epoch 2/10, Loss: 1.266737527317471
Epoch 3/10, Loss: 0.789987196524938
Epoch 4/10, Loss: 0.39316827555497486
Epoch 5/10, Loss: 0.22527901579936346
Epoch 6/10, Loss: 0.13456016240848434
Epoch 7/10, Loss: 0.08409149147984055
Epoch 8/10, Loss: 0.08291621609694427
Epoch 9/10, Loss: 0.03362502499173085
Epoch 10/10, Loss: 0.18828200735151768
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.84 0.84 0.84 19
Pipes 1.00 0.82 0.90 17
Rockets 0.77 0.91 0.83 11
Vehicles 0.85 0.92 0.88 12
accuracy 0.87 70
macro avg 0.87 0.88 0.87 70
weighted avg 0.88 0.87 0.87 70
Training vgg with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.4802160792880588
Epoch 2/10, Loss: 0.8013126916355557
Epoch 3/10, Loss: 0.36176661650339764
Epoch 4/10, Loss: 0.14164238640417656
Epoch 5/10, Loss: 0.09615909427197443
Epoch 6/10, Loss: 0.07637973005572955
Epoch 7/10, Loss: 0.07953423344426686
Epoch 8/10, Loss: 0.03740199779470762
Epoch 9/10, Loss: 0.012463468126952648
Epoch 10/10, Loss: 0.009683569927195398
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.73 0.84 11
Mines 1.00 0.95 0.97 19
Pipes 1.00 0.94 0.97 17
Rockets 0.65 1.00 0.79 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.90 70
macro avg 0.91 0.89 0.89 70
weighted avg 0.93 0.90 0.90 70
Training vgg with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5331521431605022
Epoch 2/10, Loss: 1.1972111331091986
Epoch 3/10, Loss: 0.7533324890666537
Epoch 4/10, Loss: 0.34316211111015743
Epoch 5/10, Loss: 0.13169861709078154
Epoch 6/10, Loss: 0.06296600887758864
Epoch 7/10, Loss: 0.04212379685810043
Epoch 8/10, Loss: 0.026584373528344765
Epoch 9/10, Loss: 0.023477620251166325
Epoch 10/10, Loss: 0.09475172412607612
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.92 0.58 0.71 19
Pipes 0.89 1.00 0.94 17
Rockets 0.82 0.82 0.82 11
Vehicles 0.71 0.83 0.77 12
accuracy 0.81 70
macro avg 0.81 0.83 0.81 70
weighted avg 0.83 0.81 0.81 70
Training vgg with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6201849778493245
Epoch 2/10, Loss: 1.214341746436225
Epoch 3/10, Loss: 0.765607496102651
Epoch 4/10, Loss: 0.5091976291603513
Epoch 5/10, Loss: 0.31053388284312355
Epoch 6/10, Loss: 0.1722381506115198
Epoch 7/10, Loss: 0.15334145435028607
Epoch 8/10, Loss: 0.08702386481066544
Epoch 9/10, Loss: 0.12301970480216874
Epoch 10/10, Loss: 0.039040489277491965
Accuracy: 72.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.46 1.00 0.63 11
Mines 0.85 0.58 0.69 19
Pipes 0.93 0.76 0.84 17
Rockets 0.70 0.64 0.67 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.73 70
macro avg 0.79 0.75 0.74 70
weighted avg 0.81 0.73 0.74 70
Training vgg with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6654567188686795
Epoch 2/10, Loss: 1.6095126469930012
Epoch 3/10, Loss: 1.5346993340386286
Epoch 4/10, Loss: 1.4937675264146593
Epoch 5/10, Loss: 1.378066314591302
Epoch 6/10, Loss: 1.3626034657160442
Epoch 7/10, Loss: 1.3224547968970404
Epoch 8/10, Loss: 1.2431728310055203
Epoch 9/10, Loss: 1.176614138815138
Epoch 10/10, Loss: 1.1344768736097548
Accuracy: 50.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.43 0.91 0.59 11
Mines 0.62 0.26 0.37 19
Pipes 1.00 0.41 0.58 17
Rockets 0.33 0.36 0.35 11
Vehicles 0.45 0.75 0.56 12
accuracy 0.50 70
macro avg 0.57 0.54 0.49 70
weighted avg 0.61 0.50 0.49 70
Training vgg with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.7209761805004544
Epoch 2/10, Loss: 1.6535498301188152
Epoch 3/10, Loss: 1.5599590804841783
Epoch 4/10, Loss: 1.5082125663757324
Epoch 5/10, Loss: 1.475884993871053
Epoch 6/10, Loss: 1.4444924857881334
Epoch 7/10, Loss: 1.3826919794082642
Epoch 8/10, Loss: 1.3199271228578355
Epoch 9/10, Loss: 1.241964962747362
Epoch 10/10, Loss: 1.243576751814948
Accuracy: 47.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.73 0.59 11
Mines 0.67 0.32 0.43 19
Pipes 1.00 0.29 0.45 17
Rockets 0.57 0.36 0.44 11
Vehicles 0.30 0.83 0.44 12
accuracy 0.47 70
macro avg 0.61 0.51 0.47 70
weighted avg 0.64 0.47 0.47 70
Training vgg with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6854256921344333
Epoch 2/10, Loss: 1.6583316855960422
Epoch 3/10, Loss: 1.5911790794796414
Epoch 4/10, Loss: 1.5563287205166287
Epoch 5/10, Loss: 1.507809533013238
Epoch 6/10, Loss: 1.4929413265652127
Epoch 7/10, Loss: 1.4101214938693576
Epoch 8/10, Loss: 1.4160153733359442
Epoch 9/10, Loss: 1.320355667008294
Epoch 10/10, Loss: 1.3109313117133246
Accuracy: 52.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.62 0.73 0.67 11
Mines 0.71 0.53 0.61 19
Pipes 1.00 0.35 0.52 17
Rockets 0.40 0.36 0.38 11
Vehicles 0.33 0.75 0.46 12
accuracy 0.53 70
macro avg 0.61 0.54 0.53 70
weighted avg 0.65 0.53 0.53 70
Training vgg with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6484761105643377
Epoch 2/10, Loss: 1.578020731608073
Epoch 3/10, Loss: 1.5368991427951388
Epoch 4/10, Loss: 1.477491603957282
Epoch 5/10, Loss: 1.4302551481458876
Epoch 6/10, Loss: 1.3837830755445693
Epoch 7/10, Loss: 1.3087065882152982
Epoch 8/10, Loss: 1.2580731444888644
Epoch 9/10, Loss: 1.1764704121483698
Epoch 10/10, Loss: 1.115130258931054
Accuracy: 57.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.82 0.72 11
Mines 0.47 0.47 0.47 19
Pipes 1.00 0.41 0.58 17
Rockets 0.83 0.45 0.59 11
Vehicles 0.42 0.83 0.56 12
accuracy 0.57 70
macro avg 0.67 0.60 0.58 70
weighted avg 0.67 0.57 0.57 70
Training vgg with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.706160929467943
Epoch 2/10, Loss: 1.6298673550287883
Epoch 3/10, Loss: 1.5685307052400377
Epoch 4/10, Loss: 1.5267304446962144
Epoch 5/10, Loss: 1.445532136493259
Epoch 6/10, Loss: 1.3979965051015217
Epoch 7/10, Loss: 1.341983026928372
Epoch 8/10, Loss: 1.261701265970866
Epoch 9/10, Loss: 1.2887006998062134
Epoch 10/10, Loss: 1.1441210640801325
Accuracy: 51.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.82 0.72 11
Mines 0.62 0.26 0.37 19
Pipes 1.00 0.29 0.45 17
Rockets 0.67 0.55 0.60 11
Vehicles 0.32 0.92 0.48 12
accuracy 0.51 70
macro avg 0.65 0.57 0.52 70
weighted avg 0.67 0.51 0.50 70
Training vgg with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.777293907271491
Epoch 2/10, Loss: 1.6959444019529555
Epoch 3/10, Loss: 1.5811855792999268
Epoch 4/10, Loss: 1.5743016401926677
Epoch 5/10, Loss: 1.5067942539850872
Epoch 6/10, Loss: 1.4994143115149603
Epoch 7/10, Loss: 1.412644836637709
Epoch 8/10, Loss: 1.3817854060067072
Epoch 9/10, Loss: 1.32812942398919
Epoch 10/10, Loss: 1.2555209000905354
Accuracy: 48.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.82 0.72 11
Mines 0.86 0.32 0.46 19
Pipes 1.00 0.29 0.45 17
Rockets 0.57 0.36 0.44 11
Vehicles 0.27 0.83 0.41 12
accuracy 0.49 70
macro avg 0.67 0.53 0.50 70
weighted avg 0.71 0.49 0.49 70
Training vgg with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6207340955734253
Epoch 2/10, Loss: 1.5923110644022624
Epoch 3/10, Loss: 1.4886821111043294
Epoch 4/10, Loss: 1.4595521291097004
Epoch 5/10, Loss: 1.3890637026892767
Epoch 6/10, Loss: 1.3185039493772719
Epoch 7/10, Loss: 1.258406745062934
Epoch 8/10, Loss: 1.2020638386408489
Epoch 9/10, Loss: 1.1299894915686712
Epoch 10/10, Loss: 1.0627653532558017
Accuracy: 61.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.60 0.82 0.69 11
Mines 0.77 0.53 0.62 19
Pipes 1.00 0.65 0.79 17
Rockets 0.45 0.45 0.45 11
Vehicles 0.40 0.67 0.50 12
accuracy 0.61 70
macro avg 0.64 0.62 0.61 70
weighted avg 0.69 0.61 0.63 70
Training vgg with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6588175429238214
Epoch 2/10, Loss: 1.6054045491748385
Epoch 3/10, Loss: 1.517227225833469
Epoch 4/10, Loss: 1.5166496303346422
Epoch 5/10, Loss: 1.442365460925632
Epoch 6/10, Loss: 1.3995589415232341
Epoch 7/10, Loss: 1.3667399220996432
Epoch 8/10, Loss: 1.3376624319288466
Epoch 9/10, Loss: 1.2328277031580608
Epoch 10/10, Loss: 1.124996132320828
Accuracy: 45.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.36 0.42 11
Mines 0.67 0.42 0.52 19
Pipes 1.00 0.24 0.38 17
Rockets 0.67 0.55 0.60 11
Vehicles 0.27 0.83 0.41 12
accuracy 0.46 70
macro avg 0.62 0.48 0.47 70
weighted avg 0.65 0.46 0.46 70
Training vgg with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6901988320880466
Epoch 2/10, Loss: 1.6495175229178534
Epoch 3/10, Loss: 1.5458810196982489
Epoch 4/10, Loss: 1.5433211591508653
Epoch 5/10, Loss: 1.5198665195041232
Epoch 6/10, Loss: 1.435000287161933
Epoch 7/10, Loss: 1.3858369059032865
Epoch 8/10, Loss: 1.364730053477817
Epoch 9/10, Loss: 1.2795859972635906
Epoch 10/10, Loss: 1.297123180495368
Accuracy: 35.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.45 0.45 0.45 11
Mines 0.45 0.26 0.33 19
Pipes 1.00 0.24 0.38 17
Rockets 0.29 0.18 0.22 11
Vehicles 0.24 0.75 0.37 12
accuracy 0.36 70
macro avg 0.49 0.38 0.35 70
weighted avg 0.52 0.36 0.35 70
Training vgg with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 2.2648003101348877
Epoch 2/10, Loss: 1.6288312673568726
Epoch 3/10, Loss: 1.6175808509190877
Epoch 4/10, Loss: 1.5811390744315252
Epoch 5/10, Loss: 1.4788013431761
Epoch 6/10, Loss: 1.3444183535046048
Epoch 7/10, Loss: 1.3193804290559557
Epoch 8/10, Loss: 1.4243900378545125
Epoch 9/10, Loss: 1.3318636682298448
Epoch 10/10, Loss: 1.1529061529371474
Accuracy: 28.57%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.22 1.00 0.35 11
Vehicles 0.47 0.75 0.58 12
accuracy 0.29 70
macro avg 0.74 0.35 0.19 70
weighted avg 0.79 0.29 0.16 70
Training vgg with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 2.0436244938108654
Epoch 2/10, Loss: 1.611691368950738
Epoch 3/10, Loss: 1.61460460556878
Epoch 4/10, Loss: 1.5911302698983087
Epoch 5/10, Loss: 1.8134218851725261
Epoch 6/10, Loss: 1.551759123802185
Epoch 7/10, Loss: 1.4289818207422893
Epoch 8/10, Loss: 1.4505547020170424
Epoch 9/10, Loss: 1.2727751731872559
Epoch 10/10, Loss: 1.2459556129243639
Accuracy: 35.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.18 0.29 11
Mines 0.56 0.26 0.36 19
Pipes 1.00 0.00 0.00 17
Rockets 0.30 0.91 0.45 11
Vehicles 0.32 0.67 0.43 12
accuracy 0.36 70
macro avg 0.57 0.40 0.31 70
weighted avg 0.60 0.36 0.29 70
Training vgg with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.9078905582427979
Epoch 2/10, Loss: 1.7363796763949924
Epoch 3/10, Loss: 1.5881367789374456
Epoch 4/10, Loss: 1.7965587774912517
Epoch 5/10, Loss: 2.2992943790223865
Epoch 6/10, Loss: 1.6050336360931396
Epoch 7/10, Loss: 1.614694356918335
Epoch 8/10, Loss: 1.5665310356352065
Epoch 9/10, Loss: 1.4853123956256442
Epoch 10/10, Loss: 1.5019007126490276
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.27 0.43 11
Mines 0.44 0.21 0.29 19
Pipes 0.47 0.53 0.50 17
Rockets 0.67 0.18 0.29 11
Vehicles 0.22 0.67 0.33 12
accuracy 0.37 70
macro avg 0.56 0.37 0.37 70
weighted avg 0.54 0.37 0.37 70
Training vgg with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.8605989747577243
Epoch 2/10, Loss: 1.653696788681878
Epoch 3/10, Loss: 1.6638785468207464
Epoch 4/10, Loss: 1.4655175076590643
Epoch 5/10, Loss: 1.3752390808529324
Epoch 6/10, Loss: 1.3016799820794
Epoch 7/10, Loss: 1.2569286690817938
Epoch 8/10, Loss: 1.1796545187632244
Epoch 9/10, Loss: 1.3053633835580614
Epoch 10/10, Loss: 1.2001382112503052
Accuracy: 31.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.55 0.71 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.22 0.64 0.33 11
Vehicles 0.28 0.75 0.41 12
accuracy 0.31 70
macro avg 0.70 0.39 0.29 70
weighted avg 0.75 0.31 0.23 70
Training vgg with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 2.0578880972332425
Epoch 2/10, Loss: 1.7094260851542156
Epoch 3/10, Loss: 1.5996575487984552
Epoch 4/10, Loss: 1.5917391644583807
Epoch 5/10, Loss: 1.5588583283954196
Epoch 6/10, Loss: 1.4299894836213853
Epoch 7/10, Loss: 1.2975761890411377
Epoch 8/10, Loss: 1.181961761580573
Epoch 9/10, Loss: 1.0605816708670721
Epoch 10/10, Loss: 1.0251263048913744
Accuracy: 44.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 0.73 0.73 11
Mines 0.34 0.74 0.47 19
Pipes 1.00 0.00 0.00 17
Rockets 0.33 0.27 0.30 11
Vehicles 0.67 0.50 0.57 12
accuracy 0.44 70
macro avg 0.61 0.45 0.41 70
weighted avg 0.62 0.44 0.39 70
Training vgg with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.7836352851655748
Epoch 2/10, Loss: 1.6321125030517578
Epoch 3/10, Loss: 1.7263729969660442
Epoch 4/10, Loss: 1.6374569469028049
Epoch 5/10, Loss: 1.6047265529632568
Epoch 6/10, Loss: 1.5803560150994196
Epoch 7/10, Loss: 1.5783608357111614
Epoch 8/10, Loss: 1.375575582186381
Epoch 9/10, Loss: 1.3264371752738953
Epoch 10/10, Loss: 1.5145617326100667
Accuracy: 38.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.58 0.64 0.61 11
Mines 0.31 0.89 0.46 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 1.00 0.25 0.40 12
accuracy 0.39 70
macro avg 0.78 0.36 0.29 70
weighted avg 0.75 0.39 0.29 70
Training vgg with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.761824859513177
Epoch 2/10, Loss: 1.6185342603259616
Epoch 3/10, Loss: 1.6032837364408705
Epoch 4/10, Loss: 1.6941119035085042
Epoch 5/10, Loss: 1.569568329387241
Epoch 6/10, Loss: 1.478940725326538
Epoch 7/10, Loss: 1.4983976417117648
Epoch 8/10, Loss: 1.2281623813841078
Epoch 9/10, Loss: 1.349317689736684
Epoch 10/10, Loss: 1.2089357574780781
Accuracy: 31.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.45 0.56 11
Mines 0.50 0.05 0.10 19
Pipes 1.00 0.00 0.00 17
Rockets 0.20 0.73 0.31 11
Vehicles 0.40 0.67 0.50 12
accuracy 0.31 70
macro avg 0.56 0.38 0.29 70
weighted avg 0.59 0.31 0.25 70
Training vgg with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7490653991699219
Epoch 2/10, Loss: 1.6402081118689642
Epoch 3/10, Loss: 1.5933972862031724
Epoch 4/10, Loss: 1.6201930575900607
Epoch 5/10, Loss: 1.8591215345594618
Epoch 6/10, Loss: 1.5855382283528645
Epoch 7/10, Loss: 1.5464385483000014
Epoch 8/10, Loss: 1.7314713266160753
Epoch 9/10, Loss: 1.502417696846856
Epoch 10/10, Loss: 1.3541273408465915
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.20 0.82 0.33 11
Vehicles 0.31 0.67 0.42 12
accuracy 0.24 70
macro avg 0.70 0.30 0.15 70
weighted avg 0.76 0.24 0.12 70
Training vgg with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.7505546145968967
Epoch 2/10, Loss: 2.091862506336636
Epoch 3/10, Loss: 1.6031102869245741
Epoch 4/10, Loss: 1.624559031592475
Epoch 5/10, Loss: 1.6915313932630751
Epoch 6/10, Loss: 1.6074044704437256
Epoch 7/10, Loss: 1.602164215511746
Epoch 8/10, Loss: 1.5753385755750868
Epoch 9/10, Loss: 1.457990460925632
Epoch 10/10, Loss: 1.4116627772649128
Accuracy: 18.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.09 0.15 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.18 1.00 0.30 12
accuracy 0.19 70
macro avg 0.74 0.22 0.09 70
weighted avg 0.78 0.19 0.08 70
Training vgg with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.4721676588058472
Epoch 2/10, Loss: 0.9169520735740662
Epoch 3/10, Loss: 0.42095339894294737
Epoch 4/10, Loss: 0.19723221957683562
Epoch 5/10, Loss: 0.2262560289353132
Epoch 6/10, Loss: 0.13052067905664444
Epoch 7/10, Loss: 0.04708789885044098
Epoch 8/10, Loss: 0.029921992123126982
Epoch 9/10, Loss: 0.019087302125990392
Epoch 10/10, Loss: 0.01898680659942329
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.74 0.89 0.81 19
Pipes 1.00 0.88 0.94 17
Rockets 0.86 0.55 0.67 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.83 70
macro avg 0.84 0.81 0.82 70
weighted avg 0.84 0.83 0.82 70
Training vgg with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6059288263320923
Epoch 2/10, Loss: 1.2625807285308839
Epoch 3/10, Loss: 0.7049835205078125
Epoch 4/10, Loss: 0.5202511072158813
Epoch 5/10, Loss: 0.2781293600797653
Epoch 6/10, Loss: 0.13594357669353485
Epoch 7/10, Loss: 0.08788358382880687
Epoch 8/10, Loss: 0.06651649717241526
Epoch 9/10, Loss: 0.062327510118484496
Epoch 10/10, Loss: 0.0684262739494443
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 1.00 0.89 0.94 19
Pipes 0.94 0.94 0.94 17
Rockets 0.69 1.00 0.81 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.90 70
macro avg 0.91 0.90 0.89 70
weighted avg 0.92 0.90 0.90 70
Training vgg with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.5998515605926513
Epoch 2/10, Loss: 1.4394917488098145
Epoch 3/10, Loss: 1.0838522791862488
Epoch 4/10, Loss: 0.7069073557853699
Epoch 5/10, Loss: 0.3683739259839058
Epoch 6/10, Loss: 0.3041685551404953
Epoch 7/10, Loss: 0.15185146033763885
Epoch 8/10, Loss: 0.15522430837154388
Epoch 9/10, Loss: 0.14407473765313625
Epoch 10/10, Loss: 0.047395667247474196
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 0.88 0.94 17
Rockets 0.75 0.82 0.78 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.89 70
macro avg 0.88 0.88 0.87 70
weighted avg 0.90 0.89 0.89 70
Training vgg with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.551309084892273
Epoch 2/10, Loss: 1.1833207726478576
Epoch 3/10, Loss: 0.6605409622192383
Epoch 4/10, Loss: 0.651796555519104
Epoch 5/10, Loss: 0.3452725142240524
Epoch 6/10, Loss: 0.22315564453601838
Epoch 7/10, Loss: 0.0946173794567585
Epoch 8/10, Loss: 0.014096896955743432
Epoch 9/10, Loss: 0.009292125573847444
Epoch 10/10, Loss: 0.0017889500479213894
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.82 0.78 11
Mines 0.76 0.84 0.80 19
Pipes 1.00 0.76 0.87 17
Rockets 0.79 1.00 0.88 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.83 70
macro avg 0.84 0.83 0.83 70
weighted avg 0.85 0.83 0.83 70
Training vgg with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5995607376098633
Epoch 2/10, Loss: 1.2142467260360719
Epoch 3/10, Loss: 0.6913467645645142
Epoch 4/10, Loss: 0.45107861161231994
Epoch 5/10, Loss: 0.27042733430862426
Epoch 6/10, Loss: 0.1143668282777071
Epoch 7/10, Loss: 0.07124107331037521
Epoch 8/10, Loss: 0.03087374996393919
Epoch 9/10, Loss: 0.059154218435287474
Epoch 10/10, Loss: 0.023886597156524657
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.82 0.95 0.88 19
Pipes 1.00 0.82 0.90 17
Rockets 0.65 1.00 0.79 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.86 70
macro avg 0.89 0.85 0.85 70
weighted avg 0.90 0.86 0.86 70
Training vgg with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.600511407852173
Epoch 2/10, Loss: 1.2862818002700807
Epoch 3/10, Loss: 0.8915875434875489
Epoch 4/10, Loss: 0.5764803111553192
Epoch 5/10, Loss: 0.3364091992378235
Epoch 6/10, Loss: 0.14813475608825682
Epoch 7/10, Loss: 0.07180402148514986
Epoch 8/10, Loss: 0.09605370871722699
Epoch 9/10, Loss: 0.09100004658102989
Epoch 10/10, Loss: 0.07720285281538963
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.79 1.00 0.88 19
Pipes 1.00 0.71 0.83 17
Rockets 0.88 0.64 0.74 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.83 70
macro avg 0.85 0.82 0.82 70
weighted avg 0.86 0.83 0.83 70
Training vgg with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5392807006835938
Epoch 2/10, Loss: 1.0206466913223267
Epoch 3/10, Loss: 0.48841732144355776
Epoch 4/10, Loss: 0.2208082780241966
Epoch 5/10, Loss: 0.15436332523822785
Epoch 6/10, Loss: 0.09945651665329933
Epoch 7/10, Loss: 0.052685105800628663
Epoch 8/10, Loss: 0.012950426363386214
Epoch 9/10, Loss: 0.007843181118369102
Epoch 10/10, Loss: 0.021194755146279932
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 0.85 0.89 0.87 19
Pipes 1.00 0.94 0.97 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.90 70
macro avg 0.91 0.90 0.90 70
weighted avg 0.91 0.90 0.90 70
Training vgg with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5966549634933471
Epoch 2/10, Loss: 1.1564122200012208
Epoch 3/10, Loss: 0.5672720551490784
Epoch 4/10, Loss: 0.37688441574573517
Epoch 5/10, Loss: 0.15551786720752717
Epoch 6/10, Loss: 0.1328393802046776
Epoch 7/10, Loss: 0.1578809916973114
Epoch 8/10, Loss: 0.055389617756009105
Epoch 9/10, Loss: 0.03765912000089884
Epoch 10/10, Loss: 0.023132942244410516
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 1.00 0.89 0.94 19
Pipes 1.00 0.94 0.97 17
Rockets 0.55 1.00 0.71 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.86 70
macro avg 0.89 0.85 0.84 70
weighted avg 0.91 0.86 0.86 70
Training vgg with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6329480171203614
Epoch 2/10, Loss: 1.3476122617721558
Epoch 3/10, Loss: 0.9016932129859925
Epoch 4/10, Loss: 0.49949946999549866
Epoch 5/10, Loss: 0.3831241339445114
Epoch 6/10, Loss: 0.26795250475406646
Epoch 7/10, Loss: 0.19806382358074187
Epoch 8/10, Loss: 0.10407190471887588
Epoch 9/10, Loss: 0.04141138829290867
Epoch 10/10, Loss: 0.01251180088147521
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.89 0.84 0.86 19
Pipes 1.00 0.88 0.94 17
Rockets 0.77 0.91 0.83 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.87 70
macro avg 0.87 0.88 0.87 70
weighted avg 0.88 0.87 0.87 70
Training vgg with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6731307983398438
Epoch 2/10, Loss: 1.6460779666900636
Epoch 3/10, Loss: 1.595355224609375
Epoch 4/10, Loss: 1.599075436592102
Epoch 5/10, Loss: 1.540958285331726
Epoch 6/10, Loss: 1.5217869997024536
Epoch 7/10, Loss: 1.512345314025879
Epoch 8/10, Loss: 1.4598404169082642
Epoch 9/10, Loss: 1.3902330636978149
Epoch 10/10, Loss: 1.3926405906677246
Accuracy: 41.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.62 0.45 0.53 11
Mines 0.55 0.32 0.40 19
Pipes 1.00 0.24 0.38 17
Rockets 0.67 0.36 0.47 11
Vehicles 0.24 0.83 0.38 12
accuracy 0.41 70
macro avg 0.62 0.44 0.43 70
weighted avg 0.64 0.41 0.42 70
Training vgg with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.7109892129898072
Epoch 2/10, Loss: 1.6765783071517943
Epoch 3/10, Loss: 1.5852751970291137
Epoch 4/10, Loss: 1.5515823125839234
Epoch 5/10, Loss: 1.5752257823944091
Epoch 6/10, Loss: 1.4977513551712036
Epoch 7/10, Loss: 1.4725088119506835
Epoch 8/10, Loss: 1.4423148393630982
Epoch 9/10, Loss: 1.4254908323287965
Epoch 10/10, Loss: 1.4061488151550292
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.54 0.64 0.58 11
Mines 0.75 0.16 0.26 19
Pipes 1.00 0.24 0.38 17
Rockets 0.40 0.18 0.25 11
Vehicles 0.23 0.83 0.36 12
accuracy 0.37 70
macro avg 0.58 0.41 0.37 70
weighted avg 0.63 0.37 0.36 70
Training vgg with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.8079872608184815
Epoch 2/10, Loss: 1.7613258600234984
Epoch 3/10, Loss: 1.6660094499588012
Epoch 4/10, Loss: 1.5997125864028932
Epoch 5/10, Loss: 1.5603845357894897
Epoch 6/10, Loss: 1.556715154647827
Epoch 7/10, Loss: 1.5548514366149901
Epoch 8/10, Loss: 1.566232442855835
Epoch 9/10, Loss: 1.4801364183425902
Epoch 10/10, Loss: 1.4372702360153198
Accuracy: 44.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.64 0.56 11
Mines 0.86 0.32 0.46 19
Pipes 1.00 0.18 0.30 17
Rockets 0.56 0.45 0.50 11
Vehicles 0.27 0.83 0.41 12
accuracy 0.44 70
macro avg 0.64 0.48 0.45 70
weighted avg 0.69 0.44 0.43 70
Training vgg with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.7036102294921875
Epoch 2/10, Loss: 1.6727609872817992
Epoch 3/10, Loss: 1.6081742286682128
Epoch 4/10, Loss: 1.5762359619140625
Epoch 5/10, Loss: 1.5397549867630005
Epoch 6/10, Loss: 1.4771901369094849
Epoch 7/10, Loss: 1.4768846035003662
Epoch 8/10, Loss: 1.487355375289917
Epoch 9/10, Loss: 1.4184566020965577
Epoch 10/10, Loss: 1.3899096488952636
Accuracy: 34.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.40 0.36 0.38 11
Mines 0.38 0.16 0.22 19
Pipes 1.00 0.24 0.38 17
Rockets 0.43 0.27 0.33 11
Vehicles 0.24 0.83 0.38 12
accuracy 0.34 70
macro avg 0.49 0.37 0.34 70
weighted avg 0.52 0.34 0.33 70
Training vgg with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6614128351211548
Epoch 2/10, Loss: 1.6786219596862793
Epoch 3/10, Loss: 1.6226513385772705
Epoch 4/10, Loss: 1.6009068250656129
Epoch 5/10, Loss: 1.5672165155410767
Epoch 6/10, Loss: 1.546135950088501
Epoch 7/10, Loss: 1.4837461948394775
Epoch 8/10, Loss: 1.4772425889968872
Epoch 9/10, Loss: 1.473613405227661
Epoch 10/10, Loss: 1.3876793384552002
Accuracy: 35.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.46 0.55 0.50 11
Mines 0.50 0.05 0.10 19
Pipes 1.00 0.24 0.38 17
Rockets 0.31 0.36 0.33 11
Vehicles 0.26 0.83 0.40 12
accuracy 0.36 70
macro avg 0.51 0.41 0.34 70
weighted avg 0.54 0.36 0.32 70
Training vgg with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.744667077064514
Epoch 2/10, Loss: 1.7039663791656494
Epoch 3/10, Loss: 1.681592011451721
Epoch 4/10, Loss: 1.6759184837341308
Epoch 5/10, Loss: 1.616769790649414
Epoch 6/10, Loss: 1.5825117588043214
Epoch 7/10, Loss: 1.5803300619125367
Epoch 8/10, Loss: 1.504719614982605
Epoch 9/10, Loss: 1.541321301460266
Epoch 10/10, Loss: 1.4877611160278321
Accuracy: 47.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.82 0.67 11
Mines 1.00 0.21 0.35 19
Pipes 1.00 0.24 0.38 17
Rockets 0.50 0.45 0.48 11
Vehicles 0.31 0.92 0.46 12
accuracy 0.47 70
macro avg 0.67 0.53 0.47 70
weighted avg 0.73 0.47 0.45 70
Training vgg with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6659760236740113
Epoch 2/10, Loss: 1.6342803955078125
Epoch 3/10, Loss: 1.5788432359695435
Epoch 4/10, Loss: 1.5707878828048707
Epoch 5/10, Loss: 1.5243780851364135
Epoch 6/10, Loss: 1.4891502380371093
Epoch 7/10, Loss: 1.4761774778366088
Epoch 8/10, Loss: 1.4297703504562378
Epoch 9/10, Loss: 1.3923926591873168
Epoch 10/10, Loss: 1.3473153352737426
Accuracy: 38.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.36 0.47 11
Mines 0.43 0.32 0.36 19
Pipes 1.00 0.24 0.38 17
Rockets 0.50 0.27 0.35 11
Vehicles 0.25 0.83 0.38 12
accuracy 0.39 70
macro avg 0.57 0.40 0.39 70
weighted avg 0.59 0.39 0.39 70
Training vgg with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7244285106658936
Epoch 2/10, Loss: 1.6826258182525635
Epoch 3/10, Loss: 1.6495235443115235
Epoch 4/10, Loss: 1.6568856954574585
Epoch 5/10, Loss: 1.5022906064987183
Epoch 6/10, Loss: 1.5421044111251831
Epoch 7/10, Loss: 1.5072572946548461
Epoch 8/10, Loss: 1.4913522720336914
Epoch 9/10, Loss: 1.4672966003417969
Epoch 10/10, Loss: 1.4209341764450074
Accuracy: 48.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.55 0.63 11
Mines 0.64 0.47 0.55 19
Pipes 1.00 0.24 0.38 17
Rockets 0.60 0.27 0.38 11
Vehicles 0.31 1.00 0.47 12
accuracy 0.49 70
macro avg 0.66 0.51 0.48 70
weighted avg 0.68 0.49 0.48 70
Training vgg with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.724722695350647
Epoch 2/10, Loss: 1.6955238819122314
Epoch 3/10, Loss: 1.6552438020706177
Epoch 4/10, Loss: 1.6074231386184692
Epoch 5/10, Loss: 1.5879900455474854
Epoch 6/10, Loss: 1.6096136331558228
Epoch 7/10, Loss: 1.5101994037628175
Epoch 8/10, Loss: 1.4957061529159545
Epoch 9/10, Loss: 1.4982479810714722
Epoch 10/10, Loss: 1.4225942850112916
Accuracy: 35.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.55 0.71 11
Mines 0.50 0.11 0.17 19
Pipes 1.00 0.24 0.38 17
Rockets 0.33 0.27 0.30 11
Vehicles 0.21 0.83 0.34 12
accuracy 0.36 70
macro avg 0.61 0.40 0.38 70
weighted avg 0.62 0.36 0.36 70
Training vgg with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 3.1062424182891846
Epoch 2/10, Loss: 1.6703465223312377
Epoch 3/10, Loss: 1.5922828435897827
Epoch 4/10, Loss: 1.641888427734375
Epoch 5/10, Loss: 1.5626312732696532
Epoch 6/10, Loss: 1.5273922204971313
Epoch 7/10, Loss: 1.6793540716171265
Epoch 8/10, Loss: 1.821414017677307
Epoch 9/10, Loss: 1.5841116666793824
Epoch 10/10, Loss: 1.8927650451660156
Accuracy: 28.57%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.18 0.31 11
Mines 0.36 0.42 0.39 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.22 0.83 0.34 12
accuracy 0.29 70
macro avg 0.72 0.29 0.21 70
weighted avg 0.69 0.29 0.21 70
Training vgg with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.8742103815078734
Epoch 2/10, Loss: 1.5982054948806763
Epoch 3/10, Loss: 1.5542638540267943
Epoch 4/10, Loss: 2.3379781246185303
Epoch 5/10, Loss: 1.7922744750976562
Epoch 6/10, Loss: 1.8025711059570313
Epoch 7/10, Loss: 1.6147092819213866
Epoch 8/10, Loss: 1.5420243978500365
Epoch 9/10, Loss: 1.7285362005233764
Epoch 10/10, Loss: 1.4251741647720337
Accuracy: 21.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.18 1.00 0.30 11
Vehicles 0.50 0.33 0.40 12
accuracy 0.21 70
macro avg 0.74 0.27 0.14 70
weighted avg 0.79 0.21 0.12 70
Training vgg with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 2.1711798429489138
Epoch 2/10, Loss: 1.6022273302078247
Epoch 3/10, Loss: 1.612077283859253
Epoch 4/10, Loss: 1.6889820098876953
Epoch 5/10, Loss: 1.6016547679901123
Epoch 6/10, Loss: 1.6179476737976075
Epoch 7/10, Loss: 1.5527131795883178
Epoch 8/10, Loss: 1.8592226028442382
Epoch 9/10, Loss: 1.6225521087646484
Epoch 10/10, Loss: 1.6234328985214233
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.27 0.74 0.39 19
Pipes 1.00 0.00 0.00 17
Rockets 0.11 0.18 0.14 11
Vehicles 1.00 0.00 0.00 12
accuracy 0.23 70
macro avg 0.68 0.18 0.11 70
weighted avg 0.66 0.23 0.13 70
Training vgg with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.8280403375625611
Epoch 2/10, Loss: 1.7550275802612305
Epoch 3/10, Loss: 1.6136718034744262
Epoch 4/10, Loss: 1.593225860595703
Epoch 5/10, Loss: 1.6486479997634889
Epoch 6/10, Loss: 1.5812613725662232
Epoch 7/10, Loss: 1.635791254043579
Epoch 8/10, Loss: 1.6047876119613647
Epoch 9/10, Loss: 1.5735199689865111
Epoch 10/10, Loss: 1.5602845907211305
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.40 0.18 0.25 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.23 0.82 0.35 11
Vehicles 0.20 0.42 0.27 12
accuracy 0.23 70
macro avg 0.57 0.28 0.17 70
weighted avg 0.65 0.23 0.14 70
Training vgg with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 2.1433477640151977
Epoch 2/10, Loss: 1.7060636281967163
Epoch 3/10, Loss: 1.588004183769226
Epoch 4/10, Loss: 1.6553462266921997
Epoch 5/10, Loss: 1.5952477693557738
Epoch 6/10, Loss: 1.728743600845337
Epoch 7/10, Loss: 1.5605651617050171
Epoch 8/10, Loss: 1.4451187133789063
Epoch 9/10, Loss: 5.063524436950684
Epoch 10/10, Loss: 1.4320987224578858
Accuracy: 21.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.09 0.17 11
Mines 0.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.18 1.00 0.31 11
Vehicles 0.38 0.25 0.30 12
accuracy 0.21 70
macro avg 0.51 0.27 0.16 70
weighted avg 0.49 0.21 0.13 70
Training vgg with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.9361873149871827
Epoch 2/10, Loss: 1.5820014476776123
Epoch 3/10, Loss: 1.6422109127044677
Epoch 4/10, Loss: 1.5507178544998168
Epoch 5/10, Loss: 1.7146249055862426
Epoch 6/10, Loss: 1.3707010984420775
Epoch 7/10, Loss: 1.6643004179000855
Epoch 8/10, Loss: 1.3330929040908814
Epoch 9/10, Loss: 1.3287199974060058
Epoch 10/10, Loss: 1.4248876571655273
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.00 0.00 11
Mines 0.31 0.53 0.39 19
Pipes 1.00 0.00 0.00 17
Rockets 1.00 0.00 0.00 11
Vehicles 0.24 0.75 0.36 12
accuracy 0.27 70
macro avg 0.71 0.26 0.15 70
weighted avg 0.68 0.27 0.17 70
Training vgg with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 2.071384000778198
Epoch 2/10, Loss: 1.6183693647384643
Epoch 3/10, Loss: 1.6028972864151
Epoch 4/10, Loss: 1.6174532651901246
Epoch 5/10, Loss: 1.5871182918548583
Epoch 6/10, Loss: 7.781738758087158
Epoch 7/10, Loss: 1.6036477088928223
Epoch 8/10, Loss: 1.59955894947052
Epoch 9/10, Loss: 1.5921273469924926
Epoch 10/10, Loss: 1.5937130928039551
Accuracy: 35.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.29 0.91 0.43 11
Mines 1.00 0.11 0.19 19
Pipes 0.80 0.24 0.36 17
Rockets 0.25 0.45 0.32 11
Vehicles 0.50 0.33 0.40 12
accuracy 0.36 70
macro avg 0.57 0.41 0.34 70
weighted avg 0.64 0.36 0.33 70
Training vgg with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.8318175792694091
Epoch 2/10, Loss: 2.18286771774292
Epoch 3/10, Loss: 1.5955397605895996
Epoch 4/10, Loss: 1.6289089918136597
Epoch 5/10, Loss: 1.6037820339202882
Epoch 6/10, Loss: 1.6756419658660888
Epoch 7/10, Loss: 1.5674721956253053
Epoch 8/10, Loss: 1.563947868347168
Epoch 9/10, Loss: 1.52990562915802
Epoch 10/10, Loss: 1.4434425115585328
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.44 0.36 0.40 11
Mines 0.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.21 0.73 0.33 11
Vehicles 0.19 0.33 0.24 12
accuracy 0.23 70
macro avg 0.37 0.28 0.19 70
weighted avg 0.38 0.23 0.16 70
Training vgg with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.8067806005477904
Epoch 2/10, Loss: 1.6633049249649048
Epoch 3/10, Loss: 1.628142499923706
Epoch 4/10, Loss: 1.6200758218765259
Epoch 5/10, Loss: 1.5784517049789428
Epoch 6/10, Loss: 1.7297879934310914
Epoch 7/10, Loss: 1.6455156564712525
Epoch 8/10, Loss: 1.5933286905288697
Epoch 9/10, Loss: 1.5605769634246827
Epoch 10/10, Loss: 1.4589664697647096
Accuracy: 22.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.27 0.82 0.41 11
Mines 1.00 0.00 0.00 19
Pipes 1.00 0.00 0.00 17
Rockets 0.21 0.64 0.31 11
Vehicles 0.00 0.00 0.00 12
accuracy 0.23 70
macro avg 0.50 0.29 0.14 70
weighted avg 0.59 0.23 0.11 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.0377745479345322
Epoch 2/10, Loss: 0.5358935164080726
Epoch 3/10, Loss: 0.40466224153836566
Epoch 4/10, Loss: 0.34107424173918033
Epoch 5/10, Loss: 0.3402143004867766
Epoch 6/10, Loss: 0.20955159535838497
Epoch 7/10, Loss: 0.18409990364064774
Epoch 8/10, Loss: 0.20416776918702656
Epoch 9/10, Loss: 0.19417821636630428
Epoch 10/10, Loss: 0.15281544822371668
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.82 0.82 0.82 11
Mines 0.82 0.95 0.88 19
Pipes 1.00 0.94 0.97 17
Rockets 1.00 0.91 0.95 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.90 70
macro avg 0.91 0.89 0.90 70
weighted avg 0.91 0.90 0.90 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.166744225554996
Epoch 2/10, Loss: 0.5460352798302969
Epoch 3/10, Loss: 0.39190078982048565
Epoch 4/10, Loss: 0.21375024774008328
Epoch 5/10, Loss: 0.27627904982202584
Epoch 6/10, Loss: 0.3264427071230279
Epoch 7/10, Loss: 0.3073007793476184
Epoch 8/10, Loss: 0.20906117672307623
Epoch 9/10, Loss: 0.1634609162186583
Epoch 10/10, Loss: 0.22203428148188525
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.86 0.55 0.67 11
Mines 0.76 1.00 0.86 19
Pipes 0.94 0.88 0.91 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.84 70
macro avg 0.87 0.82 0.82 70
weighted avg 0.86 0.84 0.84 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.169654859436883
Epoch 2/10, Loss: 0.439644702606731
Epoch 3/10, Loss: 0.3749743964936998
Epoch 4/10, Loss: 0.3134365537100368
Epoch 5/10, Loss: 0.24030990815824932
Epoch 6/10, Loss: 0.2747214363060064
Epoch 7/10, Loss: 0.32342408411204815
Epoch 8/10, Loss: 0.250337325864368
Epoch 9/10, Loss: 0.2098661842238572
Epoch 10/10, Loss: 0.13445844159772
Accuracy: 94.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 1.00 0.95 0.97 19
Pipes 1.00 1.00 1.00 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.94 70
macro avg 0.93 0.94 0.93 70
weighted avg 0.95 0.94 0.94 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.1334253748257954
Epoch 2/10, Loss: 0.5033195556865798
Epoch 3/10, Loss: 0.44061310672097737
Epoch 4/10, Loss: 0.3788820943898625
Epoch 5/10, Loss: 0.3069741947369443
Epoch 6/10, Loss: 0.23158985666102833
Epoch 7/10, Loss: 0.19407277730190092
Epoch 8/10, Loss: 0.1513283360335562
Epoch 9/10, Loss: 0.10014858937615322
Epoch 10/10, Loss: 0.0990438944556647
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 1.00 0.74 0.85 19
Pipes 0.94 0.88 0.91 17
Rockets 0.82 0.82 0.82 11
Vehicles 0.86 1.00 0.92 12
accuracy 0.87 70
macro avg 0.87 0.89 0.87 70
weighted avg 0.89 0.87 0.87 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.1679697036743164
Epoch 2/10, Loss: 0.5207421729962031
Epoch 3/10, Loss: 0.2520076301362779
Epoch 4/10, Loss: 0.23899934544331497
Epoch 5/10, Loss: 0.21319528048237166
Epoch 6/10, Loss: 0.3601140185362763
Epoch 7/10, Loss: 0.4243655126127932
Epoch 8/10, Loss: 0.23328531409303346
Epoch 9/10, Loss: 0.2013352635420031
Epoch 10/10, Loss: 0.1430484689772129
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.94 0.89 0.92 19
Pipes 0.94 0.94 0.94 17
Rockets 0.92 1.00 0.96 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.90 70
macro avg 0.89 0.90 0.89 70
weighted avg 0.90 0.90 0.90 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.202941722340054
Epoch 2/10, Loss: 0.5793065677086512
Epoch 3/10, Loss: 0.42536263167858124
Epoch 4/10, Loss: 0.360718981259399
Epoch 5/10, Loss: 0.33349882687131566
Epoch 6/10, Loss: 0.3365691358016597
Epoch 7/10, Loss: 0.18909636636575064
Epoch 8/10, Loss: 0.172235906124115
Epoch 9/10, Loss: 0.2012138773376743
Epoch 10/10, Loss: 0.2002495238557458
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 0.95 0.95 0.95 19
Pipes 0.94 0.94 0.94 17
Rockets 0.71 0.91 0.80 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.89 70
macro avg 0.88 0.87 0.87 70
weighted avg 0.89 0.89 0.89 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.1449221538172827
Epoch 2/10, Loss: 0.501281573333674
Epoch 3/10, Loss: 0.37237192773156697
Epoch 4/10, Loss: 0.28908366296026444
Epoch 5/10, Loss: 0.377779268556171
Epoch 6/10, Loss: 0.2614409009822541
Epoch 7/10, Loss: 0.38515715921918553
Epoch 8/10, Loss: 0.269776308702098
Epoch 9/10, Loss: 0.14596085705690914
Epoch 10/10, Loss: 0.1869080189305047
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.94 0.89 0.92 19
Pipes 0.94 0.94 0.94 17
Rockets 0.83 0.91 0.87 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.91 70
macro avg 0.91 0.92 0.91 70
weighted avg 0.92 0.91 0.91 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.1237475176652272
Epoch 2/10, Loss: 0.44665881411896813
Epoch 3/10, Loss: 0.4512251425120566
Epoch 4/10, Loss: 0.49777104291650986
Epoch 5/10, Loss: 0.33785519376397133
Epoch 6/10, Loss: 0.2547417384468847
Epoch 7/10, Loss: 0.21513389754626486
Epoch 8/10, Loss: 0.16770958522748616
Epoch 9/10, Loss: 0.17998869085891378
Epoch 10/10, Loss: 0.15684292760367194
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.82 0.82 0.82 11
Mines 0.86 0.95 0.90 19
Pipes 0.94 1.00 0.97 17
Rockets 0.91 0.91 0.91 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.90 70
macro avg 0.91 0.88 0.89 70
weighted avg 0.90 0.90 0.90 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.206542260117001
Epoch 2/10, Loss: 0.5474117323756218
Epoch 3/10, Loss: 0.3608928430411551
Epoch 4/10, Loss: 0.3741193409595225
Epoch 5/10, Loss: 0.35233327332470155
Epoch 6/10, Loss: 0.2969145228465398
Epoch 7/10, Loss: 0.2681988961994648
Epoch 8/10, Loss: 0.23332278202805254
Epoch 9/10, Loss: 0.25893762097176576
Epoch 10/10, Loss: 0.17636431981292036
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.90 1.00 0.95 19
Pipes 0.89 0.94 0.91 17
Rockets 0.90 0.82 0.86 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.90 70
macro avg 0.90 0.88 0.89 70
weighted avg 0.90 0.90 0.90 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6045259171062045
Epoch 2/10, Loss: 1.461158321963416
Epoch 3/10, Loss: 1.3088316652509902
Epoch 4/10, Loss: 1.159053196509679
Epoch 5/10, Loss: 1.0263221992386713
Epoch 6/10, Loss: 0.8884954816765256
Epoch 7/10, Loss: 0.7833951281176673
Epoch 8/10, Loss: 0.6754321753978729
Epoch 9/10, Loss: 0.565517019894388
Epoch 10/10, Loss: 0.5046509173181322
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.55 0.63 11
Mines 0.75 0.95 0.84 19
Pipes 1.00 0.88 0.94 17
Rockets 0.83 0.91 0.87 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.83 70
macro avg 0.83 0.81 0.81 70
weighted avg 0.84 0.83 0.82 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6189212997754414
Epoch 2/10, Loss: 1.449193192852868
Epoch 3/10, Loss: 1.3209207653999329
Epoch 4/10, Loss: 1.1646238598558638
Epoch 5/10, Loss: 1.0589487353960674
Epoch 6/10, Loss: 0.891083773639467
Epoch 7/10, Loss: 0.8118829164240096
Epoch 8/10, Loss: 0.7196628252665201
Epoch 9/10, Loss: 0.5850671612554126
Epoch 10/10, Loss: 0.5152725742922889
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.84 0.84 0.84 19
Pipes 1.00 0.94 0.97 17
Rockets 0.67 0.91 0.77 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.86 70
macro avg 0.87 0.85 0.85 70
weighted avg 0.88 0.86 0.86 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6208371188905504
Epoch 2/10, Loss: 1.4680796795421176
Epoch 3/10, Loss: 1.2912955350346036
Epoch 4/10, Loss: 1.1391524738735623
Epoch 5/10, Loss: 1.03350231051445
Epoch 6/10, Loss: 0.9624692598978678
Epoch 7/10, Loss: 0.8410504526562161
Epoch 8/10, Loss: 0.69309068719546
Epoch 9/10, Loss: 0.5933086954885058
Epoch 10/10, Loss: 0.5860880398088031
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.82 0.78 11
Mines 0.84 0.84 0.84 19
Pipes 1.00 1.00 1.00 17
Rockets 0.77 0.91 0.83 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.86 70
macro avg 0.85 0.85 0.84 70
weighted avg 0.86 0.86 0.86 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5954938067330255
Epoch 2/10, Loss: 1.4516851041052077
Epoch 3/10, Loss: 1.3364665971861944
Epoch 4/10, Loss: 1.1736868619918823
Epoch 5/10, Loss: 1.0346026652389102
Epoch 6/10, Loss: 0.8869078391128116
Epoch 7/10, Loss: 0.7733288043075137
Epoch 8/10, Loss: 0.6887981560495164
Epoch 9/10, Loss: 0.6574844535854127
Epoch 10/10, Loss: 0.5140282644165887
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.89 0.89 0.89 19
Pipes 1.00 0.88 0.94 17
Rockets 0.79 1.00 0.88 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.89 70
macro avg 0.88 0.89 0.88 70
weighted avg 0.89 0.89 0.89 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6228341195318434
Epoch 2/10, Loss: 1.4974981149037678
Epoch 3/10, Loss: 1.3437359266810947
Epoch 4/10, Loss: 1.226311014758216
Epoch 5/10, Loss: 1.1289014154010348
Epoch 6/10, Loss: 0.9970919224951003
Epoch 7/10, Loss: 0.9089097347524431
Epoch 8/10, Loss: 0.7919818825191922
Epoch 9/10, Loss: 0.7320234245724149
Epoch 10/10, Loss: 0.6593061784903208
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 1.00 0.84 0.91 19
Pipes 0.89 1.00 0.94 17
Rockets 0.75 0.82 0.78 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.87 70
macro avg 0.86 0.86 0.86 70
weighted avg 0.88 0.87 0.87 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6007159286075168
Epoch 2/10, Loss: 1.4642528494199116
Epoch 3/10, Loss: 1.2944898075527616
Epoch 4/10, Loss: 1.1536504924297333
Epoch 5/10, Loss: 1.0568571951654222
Epoch 6/10, Loss: 0.9328356815709008
Epoch 7/10, Loss: 0.8365432719389597
Epoch 8/10, Loss: 0.6968928873538971
Epoch 9/10, Loss: 0.6367126339011722
Epoch 10/10, Loss: 0.543397820658154
Accuracy: 78.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.82 0.78 11
Mines 0.76 0.84 0.80 19
Pipes 1.00 0.82 0.90 17
Rockets 0.62 0.73 0.67 11
Vehicles 0.80 0.67 0.73 12
accuracy 0.79 70
macro avg 0.79 0.78 0.78 70
weighted avg 0.80 0.79 0.79 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6091389457384746
Epoch 2/10, Loss: 1.464400913980272
Epoch 3/10, Loss: 1.316387865278456
Epoch 4/10, Loss: 1.1765922804673512
Epoch 5/10, Loss: 1.0626937283409967
Epoch 6/10, Loss: 0.9225695033868154
Epoch 7/10, Loss: 0.8174850344657898
Epoch 8/10, Loss: 0.6832560234599643
Epoch 9/10, Loss: 0.5703538060188293
Epoch 10/10, Loss: 0.5237053914202584
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.64 0.64 11
Mines 0.77 0.89 0.83 19
Pipes 0.89 0.94 0.91 17
Rockets 0.67 0.55 0.60 11
Vehicles 0.80 0.67 0.73 12
accuracy 0.77 70
macro avg 0.75 0.74 0.74 70
weighted avg 0.77 0.77 0.77 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5763802131017048
Epoch 2/10, Loss: 1.4453157252735562
Epoch 3/10, Loss: 1.291544881131914
Epoch 4/10, Loss: 1.1632166869110532
Epoch 5/10, Loss: 0.9943805999226041
Epoch 6/10, Loss: 0.9056975377930535
Epoch 7/10, Loss: 0.7881634963883294
Epoch 8/10, Loss: 0.6902015176084306
Epoch 9/10, Loss: 0.6381954385174645
Epoch 10/10, Loss: 0.550538064705001
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.82 0.78 11
Mines 0.84 0.84 0.84 19
Pipes 0.94 0.94 0.94 17
Rockets 0.67 0.73 0.70 11
Vehicles 0.80 0.67 0.73 12
accuracy 0.81 70
macro avg 0.80 0.80 0.80 70
weighted avg 0.82 0.81 0.81 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.57316878106859
Epoch 2/10, Loss: 1.47781021727456
Epoch 3/10, Loss: 1.359999610318078
Epoch 4/10, Loss: 1.2262985176510282
Epoch 5/10, Loss: 1.0781695710288153
Epoch 6/10, Loss: 0.9359439777003394
Epoch 7/10, Loss: 0.8469845553239187
Epoch 8/10, Loss: 0.759496788183848
Epoch 9/10, Loss: 0.651318242152532
Epoch 10/10, Loss: 0.5488114953041077
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.83 0.79 0.81 19
Pipes 0.84 0.94 0.89 17
Rockets 0.71 0.91 0.80 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.84 70
macro avg 0.86 0.84 0.84 70
weighted avg 0.86 0.84 0.84 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.9669107463624742
Epoch 2/10, Loss: 1.3302103347248502
Epoch 3/10, Loss: 1.1101394593715668
Epoch 4/10, Loss: 0.9711687631077237
Epoch 5/10, Loss: 0.7939375158813264
Epoch 6/10, Loss: 0.8167612188392215
Epoch 7/10, Loss: 0.47979387558168834
Epoch 8/10, Loss: 0.3809380481640498
Epoch 9/10, Loss: 0.41607189261251026
Epoch 10/10, Loss: 0.48525368836190963
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.53 0.91 0.67 11
Mines 1.00 0.47 0.64 19
Pipes 1.00 0.65 0.79 17
Rockets 0.53 0.91 0.67 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.70 70
macro avg 0.76 0.74 0.70 70
weighted avg 0.81 0.70 0.70 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.9141966501871746
Epoch 2/10, Loss: 1.3382665349377527
Epoch 3/10, Loss: 1.2632500694857702
Epoch 4/10, Loss: 1.0208845833937328
Epoch 5/10, Loss: 0.7372801394926177
Epoch 6/10, Loss: 0.5697394361098608
Epoch 7/10, Loss: 0.5815598087178336
Epoch 8/10, Loss: 0.2817826869173182
Epoch 9/10, Loss: 0.3065267505331172
Epoch 10/10, Loss: 0.35206950911217266
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.80 0.73 0.76 11
Mines 0.82 0.95 0.88 19
Pipes 0.75 0.88 0.81 17
Rockets 1.00 0.55 0.71 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.81 70
macro avg 0.84 0.79 0.80 70
weighted avg 0.83 0.81 0.81 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.9038307666778564
Epoch 2/10, Loss: 1.442834496498108
Epoch 3/10, Loss: 1.0439859628677368
Epoch 4/10, Loss: 0.7987886948717965
Epoch 5/10, Loss: 0.7099896752172046
Epoch 6/10, Loss: 0.5347453409598933
Epoch 7/10, Loss: 0.35604505572054124
Epoch 8/10, Loss: 0.30261031289895374
Epoch 9/10, Loss: 0.2657446703977055
Epoch 10/10, Loss: 0.2978382781147957
Accuracy: 68.57%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.77 0.89 0.83 19
Pipes 1.00 0.24 0.38 17
Rockets 0.42 1.00 0.59 11
Vehicles 0.78 0.58 0.67 12
accuracy 0.69 70
macro avg 0.79 0.71 0.67 70
weighted avg 0.81 0.69 0.67 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.9689954386817083
Epoch 2/10, Loss: 1.374613301621543
Epoch 3/10, Loss: 1.2647445533010695
Epoch 4/10, Loss: 0.9806912276479933
Epoch 5/10, Loss: 0.8483821120527055
Epoch 6/10, Loss: 0.47780486775769127
Epoch 7/10, Loss: 0.529585493935479
Epoch 8/10, Loss: 0.40838945532838505
Epoch 9/10, Loss: 0.250204981615146
Epoch 10/10, Loss: 0.20995521917939186
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.59 0.91 0.71 11
Mines 0.88 0.74 0.80 19
Pipes 1.00 0.82 0.90 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.81 70
macro avg 0.84 0.83 0.81 70
weighted avg 0.86 0.81 0.82 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.7985709706942241
Epoch 2/10, Loss: 1.421563724676768
Epoch 3/10, Loss: 1.094207035170661
Epoch 4/10, Loss: 0.7932416631115807
Epoch 5/10, Loss: 0.7050043145815531
Epoch 6/10, Loss: 0.5748546255959405
Epoch 7/10, Loss: 0.491713747382164
Epoch 8/10, Loss: 0.374636663744847
Epoch 9/10, Loss: 0.2993342046522432
Epoch 10/10, Loss: 0.18271364250944722
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.70 0.64 0.67 11
Mines 0.75 0.95 0.84 19
Pipes 0.88 0.82 0.85 17
Rockets 0.62 0.73 0.67 11
Vehicles 0.86 0.50 0.63 12
accuracy 0.76 70
macro avg 0.76 0.73 0.73 70
weighted avg 0.77 0.76 0.75 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.7590024338828192
Epoch 2/10, Loss: 1.3727425171269312
Epoch 3/10, Loss: 1.0688875880506303
Epoch 4/10, Loss: 0.7135641160938475
Epoch 5/10, Loss: 0.560444230834643
Epoch 6/10, Loss: 0.5417242017057207
Epoch 7/10, Loss: 0.4063688909841908
Epoch 8/10, Loss: 0.4081194880935881
Epoch 9/10, Loss: 0.18674117078383765
Epoch 10/10, Loss: 0.2228057999163866
Accuracy: 65.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.53 0.73 0.62 11
Mines 0.52 0.79 0.62 19
Pipes 0.86 0.71 0.77 17
Rockets 0.83 0.45 0.59 11
Vehicles 1.00 0.50 0.67 12
accuracy 0.66 70
macro avg 0.75 0.64 0.65 70
weighted avg 0.73 0.66 0.66 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.9057422743903265
Epoch 2/10, Loss: 1.3842748800913494
Epoch 3/10, Loss: 1.0149916178650327
Epoch 4/10, Loss: 0.9395973285039266
Epoch 5/10, Loss: 0.6094445238510767
Epoch 6/10, Loss: 0.6439137442244424
Epoch 7/10, Loss: 0.48736773679653805
Epoch 8/10, Loss: 0.41559064181314576
Epoch 9/10, Loss: 0.38919878792431617
Epoch 10/10, Loss: 0.2723770986000697
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.80 0.73 0.76 11
Mines 0.71 0.89 0.79 19
Pipes 0.83 0.88 0.86 17
Rockets 0.89 0.73 0.80 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.81 70
macro avg 0.85 0.80 0.81 70
weighted avg 0.83 0.81 0.82 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.8489890893300374
Epoch 2/10, Loss: 1.4123396343655057
Epoch 3/10, Loss: 1.0895257459746466
Epoch 4/10, Loss: 0.8705457399288813
Epoch 5/10, Loss: 0.6724164129959213
Epoch 6/10, Loss: 0.5292968170510398
Epoch 7/10, Loss: 0.3951389607455995
Epoch 8/10, Loss: 0.39506153927909005
Epoch 9/10, Loss: 0.4166228568388356
Epoch 10/10, Loss: 0.3135749109917217
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 0.83 0.79 0.81 19
Pipes 0.89 0.94 0.91 17
Rockets 0.67 0.73 0.70 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.83 70
macro avg 0.82 0.82 0.82 70
weighted avg 0.83 0.83 0.83 70
Training efficientnet with lr=0.001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.8712692393196955
Epoch 2/10, Loss: 1.4923712379402585
Epoch 3/10, Loss: 1.1790069838364918
Epoch 4/10, Loss: 1.0205821461147733
Epoch 5/10, Loss: 0.7010924940307935
Epoch 6/10, Loss: 0.7493214209874471
Epoch 7/10, Loss: 0.46225301134917474
Epoch 8/10, Loss: 0.3890584148466587
Epoch 9/10, Loss: 0.3826661648021804
Epoch 10/10, Loss: 0.4341210000630882
Accuracy: 47.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.36 0.73 0.48 11
Mines 1.00 0.05 0.10 19
Pipes 0.92 0.65 0.76 17
Rockets 0.67 0.36 0.47 11
Vehicles 0.31 0.75 0.44 12
accuracy 0.47 70
macro avg 0.65 0.51 0.45 70
weighted avg 0.71 0.47 0.44 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.202640328142378
Epoch 2/10, Loss: 0.47297506199942696
Epoch 3/10, Loss: 0.15857253265049723
Epoch 4/10, Loss: 0.09138890728354454
Epoch 5/10, Loss: 0.09007522008485264
Epoch 6/10, Loss: 0.08249753972308503
Epoch 7/10, Loss: 0.09516534064379004
Epoch 8/10, Loss: 0.20886543600095642
Epoch 9/10, Loss: 0.26800014782283044
Epoch 10/10, Loss: 0.15324627442492378
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.75 0.55 0.63 11
Mines 0.71 0.89 0.79 19
Pipes 0.87 0.76 0.81 17
Rockets 0.78 0.64 0.70 11
Vehicles 0.71 0.83 0.77 12
accuracy 0.76 70
macro avg 0.76 0.73 0.74 70
weighted avg 0.77 0.76 0.75 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.2250642710261874
Epoch 2/10, Loss: 0.39790960152943927
Epoch 3/10, Loss: 0.20315521707137427
Epoch 4/10, Loss: 0.20153195410966873
Epoch 5/10, Loss: 0.21286608444319832
Epoch 6/10, Loss: 0.17156710392898983
Epoch 7/10, Loss: 0.104327033377356
Epoch 8/10, Loss: 0.13460528353850046
Epoch 9/10, Loss: 0.0977680291980505
Epoch 10/10, Loss: 0.059886462106886834
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.94 0.89 0.92 19
Pipes 0.83 0.88 0.86 17
Rockets 0.77 0.91 0.83 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.87 70
macro avg 0.88 0.87 0.87 70
weighted avg 0.88 0.87 0.87 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.2783126102553473
Epoch 2/10, Loss: 0.5234515004687839
Epoch 3/10, Loss: 0.19937228163083395
Epoch 4/10, Loss: 0.21564726986818844
Epoch 5/10, Loss: 0.1291995553506745
Epoch 6/10, Loss: 0.13843883035911453
Epoch 7/10, Loss: 0.051500873640179634
Epoch 8/10, Loss: 0.09567539931999312
Epoch 9/10, Loss: 0.1186391558084223
Epoch 10/10, Loss: 0.1299764048308134
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 0.82 0.74 0.78 19
Pipes 1.00 0.76 0.87 17
Rockets 0.59 0.91 0.71 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.80 70
macro avg 0.82 0.81 0.80 70
weighted avg 0.83 0.80 0.81 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.174184348848131
Epoch 2/10, Loss: 0.353144528137313
Epoch 3/10, Loss: 0.14837640606694752
Epoch 4/10, Loss: 0.1256093548403846
Epoch 5/10, Loss: 0.15239113734828102
Epoch 6/10, Loss: 0.2504127530588044
Epoch 7/10, Loss: 0.2866256024895443
Epoch 8/10, Loss: 0.19077555421325895
Epoch 9/10, Loss: 0.21184956034024557
Epoch 10/10, Loss: 0.10145009433229764
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 0.94 0.97 17
Rockets 0.83 0.91 0.87 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.93 70
macro avg 0.93 0.93 0.92 70
weighted avg 0.94 0.93 0.93 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.235540012518565
Epoch 2/10, Loss: 0.3896799816025628
Epoch 3/10, Loss: 0.23328433516952726
Epoch 4/10, Loss: 0.08302129908568329
Epoch 5/10, Loss: 0.09568531376620133
Epoch 6/10, Loss: 0.19174395956926876
Epoch 7/10, Loss: 0.12080304893768495
Epoch 8/10, Loss: 0.14876323400272262
Epoch 9/10, Loss: 0.10528857964608404
Epoch 10/10, Loss: 0.10507807859943973
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.81 0.68 0.74 19
Pipes 1.00 0.76 0.87 17
Rockets 0.65 1.00 0.79 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.81 70
macro avg 0.83 0.84 0.82 70
weighted avg 0.84 0.81 0.82 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.2886323597696092
Epoch 2/10, Loss: 0.5372209880087111
Epoch 3/10, Loss: 0.21020442412959206
Epoch 4/10, Loss: 0.08243628322250313
Epoch 5/10, Loss: 0.09568977211084631
Epoch 6/10, Loss: 0.0652268494789799
Epoch 7/10, Loss: 0.07552592725389534
Epoch 8/10, Loss: 0.08687206005884542
Epoch 9/10, Loss: 0.11772988674541314
Epoch 10/10, Loss: 0.15321182873513964
Accuracy: 72.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.86 0.55 0.67 11
Mines 0.57 0.84 0.68 19
Pipes 1.00 0.71 0.83 17
Rockets 0.67 0.73 0.70 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.73 70
macro avg 0.78 0.71 0.73 70
weighted avg 0.78 0.73 0.73 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.1831590334574382
Epoch 2/10, Loss: 0.43974434667163426
Epoch 3/10, Loss: 0.1748818059762319
Epoch 4/10, Loss: 0.16369223594665527
Epoch 5/10, Loss: 0.1945678550336096
Epoch 6/10, Loss: 0.13198185049825245
Epoch 7/10, Loss: 0.14031966092685857
Epoch 8/10, Loss: 0.14422601337234178
Epoch 9/10, Loss: 0.10214408342209128
Epoch 10/10, Loss: 0.06150615598178572
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.86 1.00 0.93 19
Pipes 1.00 0.82 0.90 17
Rockets 0.75 0.82 0.78 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.89 70
macro avg 0.89 0.88 0.88 70
weighted avg 0.90 0.89 0.89 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.2926120625601873
Epoch 2/10, Loss: 0.43976856602562797
Epoch 3/10, Loss: 0.18370290597279867
Epoch 4/10, Loss: 0.1851969212293625
Epoch 5/10, Loss: 0.13143166816896862
Epoch 6/10, Loss: 0.16621005100508532
Epoch 7/10, Loss: 0.14772595123698315
Epoch 8/10, Loss: 0.14387093463705647
Epoch 9/10, Loss: 0.1253452899141444
Epoch 10/10, Loss: 0.0782372798356745
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.90 0.95 0.92 19
Pipes 1.00 0.88 0.94 17
Rockets 0.90 0.82 0.86 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.90 70
macro avg 0.91 0.90 0.89 70
weighted avg 0.92 0.90 0.90 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.309626989894443
Epoch 2/10, Loss: 0.5673839002847672
Epoch 3/10, Loss: 0.23872286164098316
Epoch 4/10, Loss: 0.14038661701811683
Epoch 5/10, Loss: 0.13487071047226587
Epoch 6/10, Loss: 0.1841401689582401
Epoch 7/10, Loss: 0.16031374906500181
Epoch 8/10, Loss: 0.052046266487903066
Epoch 9/10, Loss: 0.04352849685690469
Epoch 10/10, Loss: 0.0914501150449117
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.84 0.84 0.84 19
Pipes 1.00 0.82 0.90 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.87 70
macro avg 0.87 0.88 0.87 70
weighted avg 0.88 0.87 0.87 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6051335334777832
Epoch 2/10, Loss: 1.537533574634128
Epoch 3/10, Loss: 1.4549084371990628
Epoch 4/10, Loss: 1.3670744233661227
Epoch 5/10, Loss: 1.2582610183291965
Epoch 6/10, Loss: 1.1815290186140273
Epoch 7/10, Loss: 1.1051028701994154
Epoch 8/10, Loss: 1.0111556649208069
Epoch 9/10, Loss: 0.9238794247309366
Epoch 10/10, Loss: 0.8379330502616035
Accuracy: 64.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 0.82 0.75 11
Mines 0.52 0.79 0.62 19
Pipes 0.89 0.47 0.62 17
Rockets 1.00 0.27 0.43 11
Vehicles 0.62 0.83 0.71 12
accuracy 0.64 70
macro avg 0.74 0.64 0.63 70
weighted avg 0.73 0.64 0.63 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6073739926020305
Epoch 2/10, Loss: 1.5540113581551447
Epoch 3/10, Loss: 1.4715821345647175
Epoch 4/10, Loss: 1.3839591874016657
Epoch 5/10, Loss: 1.2859129773245916
Epoch 6/10, Loss: 1.2008250289493136
Epoch 7/10, Loss: 1.1371602018674214
Epoch 8/10, Loss: 1.0594599511888292
Epoch 9/10, Loss: 0.9686329232321845
Epoch 10/10, Loss: 0.9256352914704217
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.73 0.70 11
Mines 0.65 0.79 0.71 19
Pipes 0.92 0.65 0.76 17
Rockets 0.60 0.55 0.57 11
Vehicles 0.69 0.75 0.72 12
accuracy 0.70 70
macro avg 0.71 0.69 0.69 70
weighted avg 0.72 0.70 0.70 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6277369525697496
Epoch 2/10, Loss: 1.543851865662469
Epoch 3/10, Loss: 1.4775992896821764
Epoch 4/10, Loss: 1.3819783793555365
Epoch 5/10, Loss: 1.299437125523885
Epoch 6/10, Loss: 1.231005973286099
Epoch 7/10, Loss: 1.1245810058381822
Epoch 8/10, Loss: 1.0621043377452426
Epoch 9/10, Loss: 0.9631979995303683
Epoch 10/10, Loss: 0.9191453920470344
Accuracy: 64.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.64 0.56 11
Mines 0.71 0.63 0.67 19
Pipes 0.92 0.71 0.80 17
Rockets 0.60 0.55 0.57 11
Vehicles 0.50 0.67 0.57 12
accuracy 0.64 70
macro avg 0.65 0.64 0.63 70
weighted avg 0.67 0.64 0.65 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6223100423812866
Epoch 2/10, Loss: 1.5395859479904175
Epoch 3/10, Loss: 1.4792702462938097
Epoch 4/10, Loss: 1.3734717633989122
Epoch 5/10, Loss: 1.294873793919881
Epoch 6/10, Loss: 1.186030109723409
Epoch 7/10, Loss: 1.1045985619227092
Epoch 8/10, Loss: 1.0201763312021892
Epoch 9/10, Loss: 0.9651425017250909
Epoch 10/10, Loss: 0.896939734617869
Accuracy: 68.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.91 0.65 11
Mines 0.91 0.53 0.67 19
Pipes 1.00 0.76 0.87 17
Rockets 0.54 0.64 0.58 11
Vehicles 0.62 0.67 0.64 12
accuracy 0.69 70
macro avg 0.71 0.70 0.68 70
weighted avg 0.76 0.69 0.69 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6416608757442899
Epoch 2/10, Loss: 1.5564303795496623
Epoch 3/10, Loss: 1.4786831008063421
Epoch 4/10, Loss: 1.3714732726414998
Epoch 5/10, Loss: 1.2711470524470012
Epoch 6/10, Loss: 1.1885455184512668
Epoch 7/10, Loss: 1.1158918937047322
Epoch 8/10, Loss: 1.0286126732826233
Epoch 9/10, Loss: 0.9530036052068075
Epoch 10/10, Loss: 0.9111449586020576
Accuracy: 65.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.82 0.62 11
Mines 0.67 0.63 0.65 19
Pipes 0.90 0.53 0.67 17
Rockets 0.67 0.73 0.70 11
Vehicles 0.67 0.67 0.67 12
accuracy 0.66 70
macro avg 0.68 0.67 0.66 70
weighted avg 0.70 0.66 0.66 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6330320305294461
Epoch 2/10, Loss: 1.5702907111909654
Epoch 3/10, Loss: 1.4609820180469089
Epoch 4/10, Loss: 1.3838639656702678
Epoch 5/10, Loss: 1.2786524693171184
Epoch 6/10, Loss: 1.1942809687720404
Epoch 7/10, Loss: 1.1187782420052423
Epoch 8/10, Loss: 1.0709653562969632
Epoch 9/10, Loss: 0.9444610807630751
Epoch 10/10, Loss: 0.9110978775554233
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.62 0.73 0.67 11
Mines 0.86 0.63 0.73 19
Pipes 0.89 0.94 0.91 17
Rockets 0.36 0.36 0.36 11
Vehicles 0.64 0.75 0.69 12
accuracy 0.70 70
macro avg 0.67 0.68 0.67 70
weighted avg 0.71 0.70 0.70 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6628718376159668
Epoch 2/10, Loss: 1.5845474402109783
Epoch 3/10, Loss: 1.4859472778108385
Epoch 4/10, Loss: 1.4116103914048936
Epoch 5/10, Loss: 1.321847399075826
Epoch 6/10, Loss: 1.245704213778178
Epoch 7/10, Loss: 1.1496784422132704
Epoch 8/10, Loss: 1.0607201059659321
Epoch 9/10, Loss: 1.001893983946906
Epoch 10/10, Loss: 0.9141947560840182
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.59 0.91 0.71 11
Mines 0.88 0.74 0.80 19
Pipes 1.00 0.88 0.94 17
Rockets 0.64 0.64 0.64 11
Vehicles 0.73 0.67 0.70 12
accuracy 0.77 70
macro avg 0.77 0.77 0.76 70
weighted avg 0.80 0.77 0.78 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.631547424528334
Epoch 2/10, Loss: 1.5544371869828966
Epoch 3/10, Loss: 1.4753990835613675
Epoch 4/10, Loss: 1.4020024140675862
Epoch 5/10, Loss: 1.3097852733400133
Epoch 6/10, Loss: 1.2179161707560222
Epoch 7/10, Loss: 1.14287441306644
Epoch 8/10, Loss: 1.0427006350623236
Epoch 9/10, Loss: 0.9722426666153802
Epoch 10/10, Loss: 0.8951230976316664
Accuracy: 67.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.47 0.64 0.54 11
Mines 0.65 0.79 0.71 19
Pipes 1.00 0.82 0.90 17
Rockets 0.50 0.27 0.35 11
Vehicles 0.67 0.67 0.67 12
accuracy 0.67 70
macro avg 0.66 0.64 0.64 70
weighted avg 0.69 0.67 0.67 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6113361385133531
Epoch 2/10, Loss: 1.5640868875715468
Epoch 3/10, Loss: 1.4440199269188776
Epoch 4/10, Loss: 1.3630528847376506
Epoch 5/10, Loss: 1.2736421955956354
Epoch 6/10, Loss: 1.1913195318645902
Epoch 7/10, Loss: 1.095502409670088
Epoch 8/10, Loss: 1.0203036997053359
Epoch 9/10, Loss: 0.9300333129035102
Epoch 10/10, Loss: 0.8664596345689561
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.82 0.62 11
Mines 0.76 0.68 0.72 19
Pipes 1.00 0.82 0.90 17
Rockets 0.69 0.82 0.75 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.76 70
macro avg 0.79 0.76 0.76 70
weighted avg 0.81 0.76 0.77 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.835429072380066
Epoch 2/10, Loss: 1.2931629684236314
Epoch 3/10, Loss: 1.1048544380399916
Epoch 4/10, Loss: 1.020804888672299
Epoch 5/10, Loss: 0.7504327032301161
Epoch 6/10, Loss: 0.5073845220936669
Epoch 7/10, Loss: 0.3967044386598799
Epoch 8/10, Loss: 0.3888437839017974
Epoch 9/10, Loss: 0.3023528705040614
Epoch 10/10, Loss: 0.21804801043536928
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 0.74 0.89 0.81 19
Pipes 0.93 0.76 0.84 17
Rockets 0.83 0.91 0.87 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.83 70
macro avg 0.84 0.83 0.83 70
weighted avg 0.84 0.83 0.83 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.9469492832819622
Epoch 2/10, Loss: 1.5015434953901503
Epoch 3/10, Loss: 1.149131370915307
Epoch 4/10, Loss: 1.0017316275172763
Epoch 5/10, Loss: 0.816617015335295
Epoch 6/10, Loss: 0.5776214897632599
Epoch 7/10, Loss: 0.3965679638915592
Epoch 8/10, Loss: 0.5136951994564798
Epoch 9/10, Loss: 0.18663654973109564
Epoch 10/10, Loss: 0.25047703335682553
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.88 0.64 0.74 11
Mines 0.65 0.89 0.76 19
Pipes 1.00 0.59 0.74 17
Rockets 0.67 0.91 0.77 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.76 70
macro avg 0.80 0.76 0.76 70
weighted avg 0.80 0.76 0.76 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.8680553568734064
Epoch 2/10, Loss: 1.3599286079406738
Epoch 3/10, Loss: 0.8514479729864333
Epoch 4/10, Loss: 0.6604575316111246
Epoch 5/10, Loss: 0.4499015791548623
Epoch 6/10, Loss: 0.2815421215362019
Epoch 7/10, Loss: 0.29339441574282116
Epoch 8/10, Loss: 0.25225844979286194
Epoch 9/10, Loss: 0.20556559330887264
Epoch 10/10, Loss: 0.09462129986948437
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 1.00 0.74 0.85 19
Pipes 0.94 0.94 0.94 17
Rockets 0.73 1.00 0.85 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.86 70
macro avg 0.85 0.87 0.85 70
weighted avg 0.88 0.86 0.86 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.949467142422994
Epoch 2/10, Loss: 1.276187691423628
Epoch 3/10, Loss: 1.0103407932652368
Epoch 4/10, Loss: 0.6563539405663809
Epoch 5/10, Loss: 0.38836559653282166
Epoch 6/10, Loss: 0.32582928902573055
Epoch 7/10, Loss: 0.1889568352037006
Epoch 8/10, Loss: 0.17788491729233
Epoch 9/10, Loss: 0.17037809267640114
Epoch 10/10, Loss: 0.42359399216042626
Accuracy: 42.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.27 0.43 11
Mines 0.33 0.21 0.26 19
Pipes 1.00 0.24 0.38 17
Rockets 0.34 1.00 0.51 11
Vehicles 0.42 0.67 0.52 12
accuracy 0.43 70
macro avg 0.62 0.48 0.42 70
weighted avg 0.62 0.43 0.40 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.9405344989564683
Epoch 2/10, Loss: 1.4704093138376872
Epoch 3/10, Loss: 1.0953984061876934
Epoch 4/10, Loss: 0.869919134510888
Epoch 5/10, Loss: 0.5863386227024926
Epoch 6/10, Loss: 0.40523410505718654
Epoch 7/10, Loss: 0.43577490746974945
Epoch 8/10, Loss: 0.19851185381412506
Epoch 9/10, Loss: 0.13530458861755001
Epoch 10/10, Loss: 0.14809019284115899
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 0.94 0.97 17
Rockets 0.73 1.00 0.85 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.91 70
macro avg 0.92 0.91 0.91 70
weighted avg 0.93 0.91 0.92 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.8010118669933743
Epoch 2/10, Loss: 1.6028856171502008
Epoch 3/10, Loss: 1.390852451324463
Epoch 4/10, Loss: 1.3359065188301935
Epoch 5/10, Loss: 0.9847215811411539
Epoch 6/10, Loss: 0.6531505551603105
Epoch 7/10, Loss: 0.47954125702381134
Epoch 8/10, Loss: 0.40084601442019147
Epoch 9/10, Loss: 0.18067137317525017
Epoch 10/10, Loss: 0.30904316074318355
Accuracy: 78.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.62 0.91 0.74 11
Mines 0.68 0.89 0.77 19
Pipes 1.00 0.65 0.79 17
Rockets 1.00 0.73 0.84 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.79 70
macro avg 0.84 0.79 0.79 70
weighted avg 0.84 0.79 0.79 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.9819344679514568
Epoch 2/10, Loss: 1.555140044954088
Epoch 3/10, Loss: 1.1586441066530015
Epoch 4/10, Loss: 0.861986862288581
Epoch 5/10, Loss: 0.672276904185613
Epoch 6/10, Loss: 0.5511319504843818
Epoch 7/10, Loss: 0.5111364755365584
Epoch 8/10, Loss: 0.30292466448413
Epoch 9/10, Loss: 0.36669480469491744
Epoch 10/10, Loss: 0.17764231314261755
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.88 0.64 0.74 11
Mines 0.92 0.58 0.71 19
Pipes 0.88 0.82 0.85 17
Rockets 0.65 1.00 0.79 11
Vehicles 0.65 0.92 0.76 12
accuracy 0.77 70
macro avg 0.79 0.79 0.77 70
weighted avg 0.81 0.77 0.77 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.908270239830017
Epoch 2/10, Loss: 1.2737164497375488
Epoch 3/10, Loss: 0.9191499617364671
Epoch 4/10, Loss: 0.7538313666979471
Epoch 5/10, Loss: 0.5288257665104337
Epoch 6/10, Loss: 0.3610992713107003
Epoch 7/10, Loss: 0.4076664116647508
Epoch 8/10, Loss: 0.17304450853003395
Epoch 9/10, Loss: 0.1685982762525479
Epoch 10/10, Loss: 0.21806637052860525
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 0.74 0.89 0.81 19
Pipes 1.00 0.82 0.90 17
Rockets 0.64 0.82 0.72 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.83 70
macro avg 0.86 0.82 0.83 70
weighted avg 0.86 0.83 0.83 70
Training efficientnet with lr=0.001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.8268612093395658
Epoch 2/10, Loss: 1.2945471869574652
Epoch 3/10, Loss: 0.8574970695707533
Epoch 4/10, Loss: 0.6960152222050561
Epoch 5/10, Loss: 0.401954205499755
Epoch 6/10, Loss: 0.3010712539156278
Epoch 7/10, Loss: 0.3883365856276618
Epoch 8/10, Loss: 0.11281319873200522
Epoch 9/10, Loss: 0.13745047938492563
Epoch 10/10, Loss: 0.19023231665293375
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.70 0.64 0.67 11
Mines 0.78 0.95 0.86 19
Pipes 0.94 0.94 0.94 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.50 0.67 12
accuracy 0.83 70
macro avg 0.84 0.80 0.80 70
weighted avg 0.85 0.83 0.82 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.281801962852478
Epoch 2/10, Loss: 0.513225257396698
Epoch 3/10, Loss: 0.26607717871665953
Epoch 4/10, Loss: 0.1491522431373596
Epoch 5/10, Loss: 0.11621177345514297
Epoch 6/10, Loss: 0.12407526187598705
Epoch 7/10, Loss: 0.056630440428853034
Epoch 8/10, Loss: 0.033033662103116514
Epoch 9/10, Loss: 0.0439760965295136
Epoch 10/10, Loss: 0.034373081754893064
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 1.00 0.79 0.88 19
Pipes 0.94 0.94 0.94 17
Rockets 0.69 0.82 0.75 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.86 70
macro avg 0.85 0.86 0.85 70
weighted avg 0.88 0.86 0.86 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.294943594932556
Epoch 2/10, Loss: 0.5702719926834107
Epoch 3/10, Loss: 0.22218383550643922
Epoch 4/10, Loss: 0.0916937194764614
Epoch 5/10, Loss: 0.09212584495544433
Epoch 6/10, Loss: 0.05986070204526186
Epoch 7/10, Loss: 0.06243624240159988
Epoch 8/10, Loss: 0.05363156907260418
Epoch 9/10, Loss: 0.06102775186300278
Epoch 10/10, Loss: 0.04064177423715591
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 1.00 0.84 0.91 19
Pipes 0.89 1.00 0.94 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.85 0.92 0.88 12
accuracy 0.91 70
macro avg 0.92 0.92 0.91 70
weighted avg 0.92 0.91 0.91 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.3564346313476563
Epoch 2/10, Loss: 0.6649534046649933
Epoch 3/10, Loss: 0.3115876317024231
Epoch 4/10, Loss: 0.11511050760746003
Epoch 5/10, Loss: 0.08095938488841056
Epoch 6/10, Loss: 0.08211879748851061
Epoch 7/10, Loss: 0.0399586895480752
Epoch 8/10, Loss: 0.04563378244638443
Epoch 9/10, Loss: 0.04454646222293377
Epoch 10/10, Loss: 0.04601272456347942
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.90 1.00 0.95 19
Pipes 1.00 0.88 0.94 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.91 70
macro avg 0.93 0.91 0.91 70
weighted avg 0.93 0.91 0.92 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.362322163581848
Epoch 2/10, Loss: 0.5343198120594025
Epoch 3/10, Loss: 0.1855226144194603
Epoch 4/10, Loss: 0.09490906968712806
Epoch 5/10, Loss: 0.0859244653955102
Epoch 6/10, Loss: 0.039691039361059666
Epoch 7/10, Loss: 0.11063309386372566
Epoch 8/10, Loss: 0.054059981182217595
Epoch 9/10, Loss: 0.06206644242629409
Epoch 10/10, Loss: 0.10399828553199768
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.52 1.00 0.69 11
Mines 1.00 0.74 0.85 19
Pipes 0.88 0.88 0.88 17
Rockets 0.82 0.82 0.82 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.80 70
macro avg 0.84 0.80 0.79 70
weighted avg 0.87 0.80 0.81 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3282579898834228
Epoch 2/10, Loss: 0.5565848171710968
Epoch 3/10, Loss: 0.31217958927154543
Epoch 4/10, Loss: 0.1385637938976288
Epoch 5/10, Loss: 0.09320890456438065
Epoch 6/10, Loss: 0.05276395827531814
Epoch 7/10, Loss: 0.08171998281031848
Epoch 8/10, Loss: 0.11880475236102939
Epoch 9/10, Loss: 0.03970605283975601
Epoch 10/10, Loss: 0.06738196462392806
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.79 1.00 0.88 19
Pipes 0.94 0.94 0.94 17
Rockets 0.67 0.91 0.77 11
Vehicles 1.00 0.42 0.59 12
accuracy 0.84 70
macro avg 0.88 0.82 0.82 70
weighted avg 0.88 0.84 0.83 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.355654740333557
Epoch 2/10, Loss: 0.6718506455421448
Epoch 3/10, Loss: 0.31528857350349426
Epoch 4/10, Loss: 0.1814315378665924
Epoch 5/10, Loss: 0.11738193854689598
Epoch 6/10, Loss: 0.12379246801137925
Epoch 7/10, Loss: 0.10075411424040795
Epoch 8/10, Loss: 0.03447646927088499
Epoch 9/10, Loss: 0.18779240623116494
Epoch 10/10, Loss: 0.12856702040880919
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.83 0.79 0.81 19
Pipes 0.94 0.94 0.94 17
Rockets 0.78 0.64 0.70 11
Vehicles 0.64 0.58 0.61 12
accuracy 0.80 70
macro avg 0.78 0.79 0.78 70
weighted avg 0.80 0.80 0.80 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.2671195030212403
Epoch 2/10, Loss: 0.462708055973053
Epoch 3/10, Loss: 0.13905083686113356
Epoch 4/10, Loss: 0.09969482198357582
Epoch 5/10, Loss: 0.1397729240357876
Epoch 6/10, Loss: 0.0870665155351162
Epoch 7/10, Loss: 0.1508236736059189
Epoch 8/10, Loss: 0.07835918813943862
Epoch 9/10, Loss: 0.07938088960945607
Epoch 10/10, Loss: 0.1322562986984849
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 0.86 1.00 0.93 19
Pipes 0.85 1.00 0.92 17
Rockets 0.90 0.82 0.86 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.89 70
macro avg 0.90 0.86 0.87 70
weighted avg 0.90 0.89 0.88 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3180031538009644
Epoch 2/10, Loss: 0.47949365973472596
Epoch 3/10, Loss: 0.15523164570331574
Epoch 4/10, Loss: 0.0896036259829998
Epoch 5/10, Loss: 0.0828590713441372
Epoch 6/10, Loss: 0.050849289540201424
Epoch 7/10, Loss: 0.15470150485634804
Epoch 8/10, Loss: 0.2616021424531937
Epoch 9/10, Loss: 0.199207404255867
Epoch 10/10, Loss: 0.11924907118082047
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.91 0.69 11
Mines 0.83 0.79 0.81 19
Pipes 1.00 0.76 0.87 17
Rockets 0.89 0.73 0.80 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.80 70
macro avg 0.82 0.80 0.80 70
weighted avg 0.84 0.80 0.81 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.386406707763672
Epoch 2/10, Loss: 0.6525849938392639
Epoch 3/10, Loss: 0.3179678052663803
Epoch 4/10, Loss: 0.10190772861242295
Epoch 5/10, Loss: 0.0765415996313095
Epoch 6/10, Loss: 0.04120696652680635
Epoch 7/10, Loss: 0.02150712292641401
Epoch 8/10, Loss: 0.039804613403975966
Epoch 9/10, Loss: 0.020078321173787116
Epoch 10/10, Loss: 0.06847538743168116
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 0.94 0.97 17
Rockets 0.83 0.91 0.87 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.91 70
macro avg 0.91 0.91 0.90 70
weighted avg 0.92 0.91 0.91 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.61929190158844
Epoch 2/10, Loss: 1.5832964897155761
Epoch 3/10, Loss: 1.5452963829040527
Epoch 4/10, Loss: 1.4811507701873778
Epoch 5/10, Loss: 1.436978006362915
Epoch 6/10, Loss: 1.3795555353164672
Epoch 7/10, Loss: 1.32398202419281
Epoch 8/10, Loss: 1.262816858291626
Epoch 9/10, Loss: 1.230119228363037
Epoch 10/10, Loss: 1.1609884023666381
Accuracy: 52.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.35 0.82 0.49 11
Mines 0.40 0.21 0.28 19
Pipes 1.00 0.71 0.83 17
Rockets 0.27 0.27 0.27 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.53 70
macro avg 0.57 0.55 0.53 70
weighted avg 0.59 0.53 0.53 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.611901092529297
Epoch 2/10, Loss: 1.5959478855133056
Epoch 3/10, Loss: 1.5333590507507324
Epoch 4/10, Loss: 1.5113490581512452
Epoch 5/10, Loss: 1.4510580062866212
Epoch 6/10, Loss: 1.4158807516098022
Epoch 7/10, Loss: 1.3585027217864991
Epoch 8/10, Loss: 1.3183410882949829
Epoch 9/10, Loss: 1.2662654399871827
Epoch 10/10, Loss: 1.2195595264434815
Accuracy: 54.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.30 0.73 0.42 11
Mines 0.82 0.47 0.60 19
Pipes 0.90 0.53 0.67 17
Rockets 0.44 0.36 0.40 11
Vehicles 0.62 0.67 0.64 12
accuracy 0.54 70
macro avg 0.61 0.55 0.55 70
weighted avg 0.66 0.54 0.56 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6403666019439698
Epoch 2/10, Loss: 1.6191344738006592
Epoch 3/10, Loss: 1.5591844320297241
Epoch 4/10, Loss: 1.5125037908554078
Epoch 5/10, Loss: 1.466112446784973
Epoch 6/10, Loss: 1.4095974206924438
Epoch 7/10, Loss: 1.369000744819641
Epoch 8/10, Loss: 1.2937581300735475
Epoch 9/10, Loss: 1.2625890254974366
Epoch 10/10, Loss: 1.217141056060791
Accuracy: 50.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.40 0.91 0.56 11
Mines 0.44 0.42 0.43 19
Pipes 0.88 0.41 0.56 17
Rockets 0.40 0.18 0.25 11
Vehicles 0.57 0.67 0.62 12
accuracy 0.50 70
macro avg 0.54 0.52 0.48 70
weighted avg 0.56 0.50 0.49 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.662517237663269
Epoch 2/10, Loss: 1.628623867034912
Epoch 3/10, Loss: 1.570946455001831
Epoch 4/10, Loss: 1.5243350744247437
Epoch 5/10, Loss: 1.4842146635055542
Epoch 6/10, Loss: 1.417768955230713
Epoch 7/10, Loss: 1.377785587310791
Epoch 8/10, Loss: 1.316704559326172
Epoch 9/10, Loss: 1.2445774316787719
Epoch 10/10, Loss: 1.1905692577362061
Accuracy: 40.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.35 0.82 0.49 11
Mines 0.44 0.21 0.29 19
Pipes 1.00 0.29 0.45 17
Rockets 0.18 0.18 0.18 11
Vehicles 0.42 0.67 0.52 12
accuracy 0.40 70
macro avg 0.48 0.43 0.38 70
weighted avg 0.52 0.40 0.38 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6200991153717041
Epoch 2/10, Loss: 1.5857987880706788
Epoch 3/10, Loss: 1.5388532161712647
Epoch 4/10, Loss: 1.4742579936981202
Epoch 5/10, Loss: 1.4405980587005616
Epoch 6/10, Loss: 1.3861802101135254
Epoch 7/10, Loss: 1.3229901552200318
Epoch 8/10, Loss: 1.2733091831207275
Epoch 9/10, Loss: 1.2165597438812257
Epoch 10/10, Loss: 1.185589647293091
Accuracy: 58.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.47 0.73 0.57 11
Mines 0.55 0.58 0.56 19
Pipes 0.92 0.65 0.76 17
Rockets 0.33 0.27 0.30 11
Vehicles 0.67 0.67 0.67 12
accuracy 0.59 70
macro avg 0.59 0.58 0.57 70
weighted avg 0.61 0.59 0.59 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6510680198669434
Epoch 2/10, Loss: 1.5884394645690918
Epoch 3/10, Loss: 1.5478071212768554
Epoch 4/10, Loss: 1.51862633228302
Epoch 5/10, Loss: 1.4439571380615235
Epoch 6/10, Loss: 1.4033079385757445
Epoch 7/10, Loss: 1.3522406816482544
Epoch 8/10, Loss: 1.2824403047561646
Epoch 9/10, Loss: 1.2184601545333862
Epoch 10/10, Loss: 1.1669944524765015
Accuracy: 55.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.41 0.82 0.55 11
Mines 0.64 0.47 0.55 19
Pipes 1.00 0.53 0.69 17
Rockets 0.44 0.36 0.40 11
Vehicles 0.50 0.67 0.57 12
accuracy 0.56 70
macro avg 0.60 0.57 0.55 70
weighted avg 0.64 0.56 0.56 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6087453603744506
Epoch 2/10, Loss: 1.5833563804626465
Epoch 3/10, Loss: 1.5337668180465698
Epoch 4/10, Loss: 1.4756133317947389
Epoch 5/10, Loss: 1.4173755168914794
Epoch 6/10, Loss: 1.3733803272247314
Epoch 7/10, Loss: 1.3021161794662475
Epoch 8/10, Loss: 1.2816482543945313
Epoch 9/10, Loss: 1.2089616537094117
Epoch 10/10, Loss: 1.1600125551223754
Accuracy: 62.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.43 0.91 0.59 11
Mines 0.80 0.63 0.71 19
Pipes 0.89 0.47 0.62 17
Rockets 0.55 0.55 0.55 11
Vehicles 0.67 0.67 0.67 12
accuracy 0.63 70
macro avg 0.67 0.64 0.62 70
weighted avg 0.70 0.63 0.63 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6452686071395874
Epoch 2/10, Loss: 1.605433964729309
Epoch 3/10, Loss: 1.5526018381118774
Epoch 4/10, Loss: 1.50839204788208
Epoch 5/10, Loss: 1.4542421340942382
Epoch 6/10, Loss: 1.4009751796722412
Epoch 7/10, Loss: 1.3525106191635132
Epoch 8/10, Loss: 1.2720016241073608
Epoch 9/10, Loss: 1.2346823692321778
Epoch 10/10, Loss: 1.1667670726776123
Accuracy: 60.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.45 0.82 0.58 11
Mines 0.53 0.47 0.50 19
Pipes 1.00 0.59 0.74 17
Rockets 0.45 0.45 0.45 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.60 70
macro avg 0.64 0.62 0.61 70
weighted avg 0.66 0.60 0.61 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.7034014940261841
Epoch 2/10, Loss: 1.6464843034744263
Epoch 3/10, Loss: 1.6069028854370118
Epoch 4/10, Loss: 1.566819930076599
Epoch 5/10, Loss: 1.491795015335083
Epoch 6/10, Loss: 1.437958335876465
Epoch 7/10, Loss: 1.3908806324005127
Epoch 8/10, Loss: 1.3319866895675658
Epoch 9/10, Loss: 1.2782005786895752
Epoch 10/10, Loss: 1.244507122039795
Accuracy: 52.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.30 0.55 0.39 11
Mines 0.50 0.37 0.42 19
Pipes 1.00 0.59 0.74 17
Rockets 0.50 0.45 0.48 11
Vehicles 0.56 0.75 0.64 12
accuracy 0.53 70
macro avg 0.57 0.54 0.53 70
weighted avg 0.60 0.53 0.54 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 2.1282654762268067
Epoch 2/10, Loss: 1.6203812837600708
Epoch 3/10, Loss: 1.2754857778549193
Epoch 4/10, Loss: 0.9219298958778381
Epoch 5/10, Loss: 0.5803431630134582
Epoch 6/10, Loss: 0.7078439712524414
Epoch 7/10, Loss: 0.4619582772254944
Epoch 8/10, Loss: 0.25380684733390807
Epoch 9/10, Loss: 0.33192047774791716
Epoch 10/10, Loss: 0.2617639511823654
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.76 0.68 0.72 19
Pipes 0.89 0.94 0.91 17
Rockets 0.50 0.73 0.59 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.77 70
macro avg 0.80 0.77 0.77 70
weighted avg 0.80 0.77 0.77 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.9670652151107788
Epoch 2/10, Loss: 1.5020998477935792
Epoch 3/10, Loss: 1.0623272061347961
Epoch 4/10, Loss: 0.7717230081558227
Epoch 5/10, Loss: 0.6431475162506104
Epoch 6/10, Loss: 0.41376283168792727
Epoch 7/10, Loss: 0.42463274002075196
Epoch 8/10, Loss: 0.3118146151304245
Epoch 9/10, Loss: 0.16526361405849457
Epoch 10/10, Loss: 0.05233080238103867
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.70 0.64 0.67 11
Mines 0.74 0.89 0.81 19
Pipes 0.93 0.82 0.88 17
Rockets 0.92 1.00 0.96 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.83 70
macro avg 0.84 0.82 0.83 70
weighted avg 0.84 0.83 0.83 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.8632369995117188
Epoch 2/10, Loss: 1.917537808418274
Epoch 3/10, Loss: 1.2466782808303833
Epoch 4/10, Loss: 1.364241623878479
Epoch 5/10, Loss: 1.0395065784454345
Epoch 6/10, Loss: 0.7037477731704712
Epoch 7/10, Loss: 0.6483325123786926
Epoch 8/10, Loss: 0.3705252081155777
Epoch 9/10, Loss: 0.1740098550915718
Epoch 10/10, Loss: 0.4239952236413956
Accuracy: 61.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.53 0.82 0.64 11
Mines 1.00 0.26 0.42 19
Pipes 1.00 0.65 0.79 17
Rockets 0.36 0.91 0.51 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.61 70
macro avg 0.76 0.66 0.62 70
weighted avg 0.81 0.61 0.62 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.9244457483291626
Epoch 2/10, Loss: 1.631856870651245
Epoch 3/10, Loss: 1.3237021565437317
Epoch 4/10, Loss: 1.1213432788848876
Epoch 5/10, Loss: 0.9010881543159485
Epoch 6/10, Loss: 0.8221197724342346
Epoch 7/10, Loss: 0.7285983860492706
Epoch 8/10, Loss: 0.8248852968215943
Epoch 9/10, Loss: 0.5895049631595611
Epoch 10/10, Loss: 0.4131381928920746
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.60 0.55 0.57 11
Mines 0.70 0.84 0.76 19
Pipes 0.88 0.82 0.85 17
Rockets 0.71 0.91 0.80 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.76 70
macro avg 0.78 0.74 0.74 70
weighted avg 0.78 0.76 0.75 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 2.0055799961090086
Epoch 2/10, Loss: 1.7240270376205444
Epoch 3/10, Loss: 1.2191094636917115
Epoch 4/10, Loss: 1.0942593216896057
Epoch 5/10, Loss: 0.7269502878189087
Epoch 6/10, Loss: 0.6251954317092896
Epoch 7/10, Loss: 0.5164297580718994
Epoch 8/10, Loss: 0.5518871903419494
Epoch 9/10, Loss: 0.4454105019569397
Epoch 10/10, Loss: 0.35335263013839724
Accuracy: 58.57%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.27 0.43 11
Mines 0.50 0.79 0.61 19
Pipes 0.72 0.76 0.74 17
Rockets 0.30 0.27 0.29 11
Vehicles 0.78 0.58 0.67 12
accuracy 0.59 70
macro avg 0.66 0.54 0.55 70
weighted avg 0.65 0.59 0.57 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.9257681131362916
Epoch 2/10, Loss: 1.570642066001892
Epoch 3/10, Loss: 1.4455238580703735
Epoch 4/10, Loss: 0.7906851887702941
Epoch 5/10, Loss: 0.6302908837795258
Epoch 6/10, Loss: 0.6213998019695282
Epoch 7/10, Loss: 0.48955618739128115
Epoch 8/10, Loss: 0.3094136923551559
Epoch 9/10, Loss: 0.15262278020381928
Epoch 10/10, Loss: 0.08187384977936744
Accuracy: 64.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.38 1.00 0.55 11
Mines 0.79 0.58 0.67 19
Pipes 1.00 0.65 0.79 17
Rockets 0.83 0.91 0.87 11
Vehicles 0.50 0.17 0.25 12
accuracy 0.64 70
macro avg 0.70 0.66 0.62 70
weighted avg 0.73 0.64 0.64 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 2.1094021558761598
Epoch 2/10, Loss: 1.5830209016799928
Epoch 3/10, Loss: 1.3240637302398681
Epoch 4/10, Loss: 0.9224723815917969
Epoch 5/10, Loss: 0.723829847574234
Epoch 6/10, Loss: 0.545645660161972
Epoch 7/10, Loss: 0.3044398337602615
Epoch 8/10, Loss: 0.2689704209566116
Epoch 9/10, Loss: 0.3634530514478683
Epoch 10/10, Loss: 0.16006873548030853
Accuracy: 60.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.64 0.64 0.64 11
Mines 0.44 0.89 0.59 19
Pipes 0.90 0.53 0.67 17
Rockets 0.67 0.18 0.29 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.60 70
macro avg 0.73 0.57 0.58 70
weighted avg 0.71 0.60 0.59 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.752650547027588
Epoch 2/10, Loss: 1.62144775390625
Epoch 3/10, Loss: 1.3798218965530396
Epoch 4/10, Loss: 1.2288920044898988
Epoch 5/10, Loss: 0.9923056125640869
Epoch 6/10, Loss: 0.9281111717224121
Epoch 7/10, Loss: 0.8715598583221436
Epoch 8/10, Loss: 0.5636991262435913
Epoch 9/10, Loss: 0.39139691591262815
Epoch 10/10, Loss: 0.373888698220253
Accuracy: 44.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.23 0.91 0.37 11
Mines 1.00 0.05 0.10 19
Pipes 1.00 0.53 0.69 17
Rockets 0.50 0.55 0.52 11
Vehicles 1.00 0.42 0.59 12
accuracy 0.44 70
macro avg 0.75 0.49 0.45 70
weighted avg 0.80 0.44 0.44 70
Training efficientnet with lr=0.001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.7730114936828614
Epoch 2/10, Loss: 2.0783127784729003
Epoch 3/10, Loss: 1.0915901780128479
Epoch 4/10, Loss: 1.0080788135528564
Epoch 5/10, Loss: 0.8703049778938293
Epoch 6/10, Loss: 0.6258694410324097
Epoch 7/10, Loss: 0.40820044875144956
Epoch 8/10, Loss: 0.6355908393859864
Epoch 9/10, Loss: 0.35164892971515654
Epoch 10/10, Loss: 0.22134948074817656
Accuracy: 64.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 1.00 0.26 0.42 19
Pipes 0.77 0.59 0.67 17
Rockets 0.42 1.00 0.59 11
Vehicles 0.62 0.83 0.71 12
accuracy 0.64 70
macro avg 0.74 0.70 0.65 70
weighted avg 0.77 0.64 0.63 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.2573871115843456
Epoch 2/10, Loss: 0.5176581955618329
Epoch 3/10, Loss: 0.24060734237233797
Epoch 4/10, Loss: 0.16059235421319804
Epoch 5/10, Loss: 0.20130681505219805
Epoch 6/10, Loss: 0.13119053944117492
Epoch 7/10, Loss: 0.18063780266998541
Epoch 8/10, Loss: 0.1169465897190902
Epoch 9/10, Loss: 0.0730588285304192
Epoch 10/10, Loss: 0.12394935787758893
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 1.00 1.00 11
Mines 0.94 0.89 0.92 19
Pipes 1.00 0.94 0.97 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.93 70
macro avg 0.94 0.93 0.93 70
weighted avg 0.94 0.93 0.93 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.278280410501692
Epoch 2/10, Loss: 0.5809471491310332
Epoch 3/10, Loss: 0.29787584849529797
Epoch 4/10, Loss: 0.1776359664897124
Epoch 5/10, Loss: 0.22082896096011004
Epoch 6/10, Loss: 0.21520751362873447
Epoch 7/10, Loss: 0.15429194633745485
Epoch 8/10, Loss: 0.13996732245302862
Epoch 9/10, Loss: 0.11053412071325713
Epoch 10/10, Loss: 0.13042559976586038
Accuracy: 94.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 1.00 1.00 11
Mines 1.00 0.95 0.97 19
Pipes 0.94 1.00 0.97 17
Rockets 0.91 0.91 0.91 11
Vehicles 0.83 0.83 0.83 12
accuracy 0.94 70
macro avg 0.94 0.94 0.94 70
weighted avg 0.94 0.94 0.94 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.356639050775104
Epoch 2/10, Loss: 0.6295682738224665
Epoch 3/10, Loss: 0.3354794325100051
Epoch 4/10, Loss: 0.26366075604326195
Epoch 5/10, Loss: 0.170131323652135
Epoch 6/10, Loss: 0.184963367982871
Epoch 7/10, Loss: 0.1318136945159899
Epoch 8/10, Loss: 0.11902928786973159
Epoch 9/10, Loss: 0.20270976579437652
Epoch 10/10, Loss: 0.14235744770202372
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.95 1.00 0.97 19
Pipes 1.00 0.94 0.97 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.93 70
macro avg 0.94 0.92 0.92 70
weighted avg 0.94 0.93 0.93 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.2056356105539534
Epoch 2/10, Loss: 0.5251146306594213
Epoch 3/10, Loss: 0.3404222273578246
Epoch 4/10, Loss: 0.23458366675509346
Epoch 5/10, Loss: 0.13658807364602885
Epoch 6/10, Loss: 0.09194552417223652
Epoch 7/10, Loss: 0.12294437894080248
Epoch 8/10, Loss: 0.06888413757810162
Epoch 9/10, Loss: 0.06621282213988404
Epoch 10/10, Loss: 0.061273357546370893
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.95 1.00 0.97 19
Pipes 1.00 0.88 0.94 17
Rockets 0.77 0.91 0.83 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.91 70
macro avg 0.91 0.91 0.90 70
weighted avg 0.92 0.91 0.92 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3127462069193523
Epoch 2/10, Loss: 0.6563777277866999
Epoch 3/10, Loss: 0.35574386682775283
Epoch 4/10, Loss: 0.20852265175845888
Epoch 5/10, Loss: 0.22934441910021836
Epoch 6/10, Loss: 0.1410364670575493
Epoch 7/10, Loss: 0.08909326222621733
Epoch 8/10, Loss: 0.07130237055631976
Epoch 9/10, Loss: 0.07199647427640027
Epoch 10/10, Loss: 0.07953360513783991
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 1.00 0.84 0.91 19
Pipes 1.00 1.00 1.00 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.91 70
macro avg 0.90 0.92 0.91 70
weighted avg 0.92 0.91 0.92 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3833379480573866
Epoch 2/10, Loss: 0.7215802702638838
Epoch 3/10, Loss: 0.3385645987259017
Epoch 4/10, Loss: 0.23141537399755585
Epoch 5/10, Loss: 0.14457560144364834
Epoch 6/10, Loss: 0.14266666304320097
Epoch 7/10, Loss: 0.11408499131600063
Epoch 8/10, Loss: 0.0848881538129515
Epoch 9/10, Loss: 0.08774639438423845
Epoch 10/10, Loss: 0.08728097187769082
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.90 1.00 0.95 19
Pipes 1.00 0.88 0.94 17
Rockets 1.00 0.82 0.90 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.91 70
macro avg 0.93 0.91 0.91 70
weighted avg 0.93 0.91 0.92 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.184047543340259
Epoch 2/10, Loss: 0.4469396265016662
Epoch 3/10, Loss: 0.29021965463956195
Epoch 4/10, Loss: 0.12460122257471085
Epoch 5/10, Loss: 0.11095133674744931
Epoch 6/10, Loss: 0.1704737865883443
Epoch 7/10, Loss: 0.16056621985303032
Epoch 8/10, Loss: 0.22832988870019713
Epoch 9/10, Loss: 0.12313993833959103
Epoch 10/10, Loss: 0.21327412935594717
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.94 0.84 0.89 19
Pipes 0.94 1.00 0.97 17
Rockets 0.83 0.91 0.87 11
Vehicles 0.88 0.58 0.70 12
accuracy 0.87 70
macro avg 0.87 0.87 0.86 70
weighted avg 0.88 0.87 0.87 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3179364403088887
Epoch 2/10, Loss: 0.6576971560716629
Epoch 3/10, Loss: 0.25258829734391636
Epoch 4/10, Loss: 0.22025797184970644
Epoch 5/10, Loss: 0.12370928956402673
Epoch 6/10, Loss: 0.12435350129898223
Epoch 7/10, Loss: 0.10061401216727164
Epoch 8/10, Loss: 0.11679629580531684
Epoch 9/10, Loss: 0.15326874578992525
Epoch 10/10, Loss: 0.1872750911861658
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.90 0.82 0.86 11
Mines 0.83 1.00 0.90 19
Pipes 1.00 0.76 0.87 17
Rockets 0.83 0.91 0.87 11
Vehicles 0.92 0.92 0.92 12
accuracy 0.89 70
macro avg 0.90 0.88 0.88 70
weighted avg 0.90 0.89 0.88 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3734340733951993
Epoch 2/10, Loss: 0.6913307499554422
Epoch 3/10, Loss: 0.29734601949652034
Epoch 4/10, Loss: 0.2835874180826876
Epoch 5/10, Loss: 0.22964255097839567
Epoch 6/10, Loss: 0.19752746251308256
Epoch 7/10, Loss: 0.14782579667452309
Epoch 8/10, Loss: 0.09413847487626804
Epoch 9/10, Loss: 0.13297831939740312
Epoch 10/10, Loss: 0.14671032943038476
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.95 1.00 0.97 19
Pipes 0.83 0.88 0.86 17
Rockets 0.70 0.64 0.67 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.87 70
macro avg 0.86 0.85 0.86 70
weighted avg 0.87 0.87 0.87 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.617444634437561
Epoch 2/10, Loss: 1.5230958196851943
Epoch 3/10, Loss: 1.450023677614
Epoch 4/10, Loss: 1.3809469143549602
Epoch 5/10, Loss: 1.2726556460062664
Epoch 6/10, Loss: 1.2120815250608656
Epoch 7/10, Loss: 1.1302367150783539
Epoch 8/10, Loss: 1.054296933942371
Epoch 9/10, Loss: 0.9715149468845792
Epoch 10/10, Loss: 0.9087142381403182
Accuracy: 68.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.73 0.70 11
Mines 0.63 0.63 0.63 19
Pipes 0.86 0.71 0.77 17
Rockets 0.70 0.64 0.67 11
Vehicles 0.60 0.75 0.67 12
accuracy 0.69 70
macro avg 0.69 0.69 0.69 70
weighted avg 0.70 0.69 0.69 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6194700863626268
Epoch 2/10, Loss: 1.5364090270466275
Epoch 3/10, Loss: 1.4673458536465962
Epoch 4/10, Loss: 1.376645737224155
Epoch 5/10, Loss: 1.3029166923628912
Epoch 6/10, Loss: 1.2249812814924452
Epoch 7/10, Loss: 1.1489168339305453
Epoch 8/10, Loss: 1.0672070284684498
Epoch 9/10, Loss: 1.0266264379024506
Epoch 10/10, Loss: 0.9418986373477511
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.57 0.73 0.64 11
Mines 0.74 0.74 0.74 19
Pipes 0.93 0.82 0.88 17
Rockets 0.50 0.45 0.48 11
Vehicles 0.67 0.67 0.67 12
accuracy 0.70 70
macro avg 0.68 0.68 0.68 70
weighted avg 0.71 0.70 0.70 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.612023777431912
Epoch 2/10, Loss: 1.5565101901690166
Epoch 3/10, Loss: 1.4571529030799866
Epoch 4/10, Loss: 1.4016258252991571
Epoch 5/10, Loss: 1.2984434366226196
Epoch 6/10, Loss: 1.2654820283253987
Epoch 7/10, Loss: 1.17430579662323
Epoch 8/10, Loss: 1.1134476992819045
Epoch 9/10, Loss: 1.0157248708936903
Epoch 10/10, Loss: 0.9595719145403968
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.58 1.00 0.73 11
Mines 0.75 0.79 0.77 19
Pipes 1.00 0.65 0.79 17
Rockets 0.82 0.82 0.82 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.77 70
macro avg 0.81 0.78 0.77 70
weighted avg 0.82 0.77 0.77 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6025246183077495
Epoch 2/10, Loss: 1.524593220816718
Epoch 3/10, Loss: 1.4307186073727078
Epoch 4/10, Loss: 1.3550695710712009
Epoch 5/10, Loss: 1.2683242162068684
Epoch 6/10, Loss: 1.2236113250255585
Epoch 7/10, Loss: 1.1492251820034451
Epoch 8/10, Loss: 1.0359171628952026
Epoch 9/10, Loss: 0.9634654853079054
Epoch 10/10, Loss: 0.9365688231256273
Accuracy: 71.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.47 0.82 0.60 11
Mines 0.83 0.79 0.81 19
Pipes 1.00 0.71 0.83 17
Rockets 0.67 0.55 0.60 11
Vehicles 0.67 0.67 0.67 12
accuracy 0.71 70
macro avg 0.73 0.71 0.70 70
weighted avg 0.76 0.71 0.72 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5807654791408114
Epoch 2/10, Loss: 1.4881611466407776
Epoch 3/10, Loss: 1.433034598827362
Epoch 4/10, Loss: 1.3367557260725234
Epoch 5/10, Loss: 1.26543923219045
Epoch 6/10, Loss: 1.1648744179142847
Epoch 7/10, Loss: 1.1244455178578694
Epoch 8/10, Loss: 1.0380570458041296
Epoch 9/10, Loss: 1.011552310652203
Epoch 10/10, Loss: 0.8788781629668342
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 0.73 0.73 11
Mines 0.70 0.84 0.76 19
Pipes 0.85 0.65 0.73 17
Rockets 0.42 0.45 0.43 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.70 70
macro avg 0.70 0.68 0.69 70
weighted avg 0.71 0.70 0.70 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.638559619585673
Epoch 2/10, Loss: 1.5535165468851726
Epoch 3/10, Loss: 1.5000142256418865
Epoch 4/10, Loss: 1.4420694708824158
Epoch 5/10, Loss: 1.361268354786767
Epoch 6/10, Loss: 1.2857440114021301
Epoch 7/10, Loss: 1.2374362018373277
Epoch 8/10, Loss: 1.1281430456373427
Epoch 9/10, Loss: 1.0775117211871676
Epoch 10/10, Loss: 1.0214107599523332
Accuracy: 72.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.60 0.82 0.69 11
Mines 0.82 0.74 0.78 19
Pipes 1.00 0.71 0.83 17
Rockets 0.57 0.73 0.64 11
Vehicles 0.67 0.67 0.67 12
accuracy 0.73 70
macro avg 0.73 0.73 0.72 70
weighted avg 0.76 0.73 0.74 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.58764867650138
Epoch 2/10, Loss: 1.5243987904654608
Epoch 3/10, Loss: 1.4419774214426677
Epoch 4/10, Loss: 1.3536373972892761
Epoch 5/10, Loss: 1.2992090649074979
Epoch 6/10, Loss: 1.199938138326009
Epoch 7/10, Loss: 1.1530417104562123
Epoch 8/10, Loss: 1.0604782667424943
Epoch 9/10, Loss: 0.9896486335330539
Epoch 10/10, Loss: 0.9636717571152581
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.82 0.74 0.78 19
Pipes 0.88 0.88 0.88 17
Rockets 0.55 0.55 0.55 11
Vehicles 0.82 0.75 0.78 12
accuracy 0.77 70
macro avg 0.76 0.76 0.76 70
weighted avg 0.78 0.77 0.77 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6125486956702337
Epoch 2/10, Loss: 1.5340601669417486
Epoch 3/10, Loss: 1.4466337230470445
Epoch 4/10, Loss: 1.3811290131674872
Epoch 5/10, Loss: 1.2801323069466486
Epoch 6/10, Loss: 1.2232247855928209
Epoch 7/10, Loss: 1.1150639487637415
Epoch 8/10, Loss: 1.0795948439174228
Epoch 9/10, Loss: 1.0171060760815938
Epoch 10/10, Loss: 0.9474690357844034
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.82 0.74 0.78 19
Pipes 0.89 0.94 0.91 17
Rockets 0.50 0.55 0.52 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.77 70
macro avg 0.78 0.76 0.76 70
weighted avg 0.79 0.77 0.77 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6322011484040155
Epoch 2/10, Loss: 1.553182926442888
Epoch 3/10, Loss: 1.4791249831517537
Epoch 4/10, Loss: 1.4183307819896274
Epoch 5/10, Loss: 1.3018541402286954
Epoch 6/10, Loss: 1.2670753399531047
Epoch 7/10, Loss: 1.1878940727975633
Epoch 8/10, Loss: 1.100712753004498
Epoch 9/10, Loss: 1.0562158988581762
Epoch 10/10, Loss: 1.0329653455151453
Accuracy: 67.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.42 0.73 0.53 11
Mines 0.88 0.74 0.80 19
Pipes 0.93 0.76 0.84 17
Rockets 0.67 0.36 0.47 11
Vehicles 0.53 0.67 0.59 12
accuracy 0.67 70
macro avg 0.68 0.65 0.65 70
weighted avg 0.73 0.67 0.68 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.467455204990175
Epoch 2/10, Loss: 0.9505860474374559
Epoch 3/10, Loss: 0.522499014933904
Epoch 4/10, Loss: 0.3981761518451903
Epoch 5/10, Loss: 0.2654180003123151
Epoch 6/10, Loss: 0.28213031714161235
Epoch 7/10, Loss: 0.1401142750142349
Epoch 8/10, Loss: 0.07813575524940258
Epoch 9/10, Loss: 0.19708535256278184
Epoch 10/10, Loss: 0.14253014848671025
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.94 0.89 0.92 19
Pipes 1.00 0.82 0.90 17
Rockets 0.75 0.82 0.78 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.86 70
macro avg 0.85 0.86 0.85 70
weighted avg 0.88 0.86 0.86 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.4841628736919827
Epoch 2/10, Loss: 0.8659636146492429
Epoch 3/10, Loss: 0.46508857276704574
Epoch 4/10, Loss: 0.3911282482246558
Epoch 5/10, Loss: 0.3387415429784192
Epoch 6/10, Loss: 0.08728641571684016
Epoch 7/10, Loss: 0.19803514745500353
Epoch 8/10, Loss: 0.17532673333254126
Epoch 9/10, Loss: 0.124648738031586
Epoch 10/10, Loss: 0.08414219794536217
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.86 1.00 0.93 19
Pipes 0.94 0.94 0.94 17
Rockets 0.91 0.91 0.91 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.93 70
macro avg 0.94 0.92 0.93 70
weighted avg 0.93 0.93 0.93 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.418371965487798
Epoch 2/10, Loss: 0.8767693440119425
Epoch 3/10, Loss: 0.37253166197074783
Epoch 4/10, Loss: 0.31789107993245125
Epoch 5/10, Loss: 0.14688753243535757
Epoch 6/10, Loss: 0.10677704856627518
Epoch 7/10, Loss: 0.1723252795636654
Epoch 8/10, Loss: 0.11144902852053444
Epoch 9/10, Loss: 0.09123349614027473
Epoch 10/10, Loss: 0.14598216716614035
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.90 1.00 0.95 19
Pipes 1.00 0.94 0.97 17
Rockets 0.83 0.91 0.87 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.91 70
macro avg 0.91 0.90 0.90 70
weighted avg 0.92 0.91 0.91 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.476508806149165
Epoch 2/10, Loss: 0.7304648872878816
Epoch 3/10, Loss: 0.4880746752023697
Epoch 4/10, Loss: 0.2761719094382392
Epoch 5/10, Loss: 0.29023875461684334
Epoch 6/10, Loss: 0.16873343200940225
Epoch 7/10, Loss: 0.11300751830761631
Epoch 8/10, Loss: 0.11517622978943917
Epoch 9/10, Loss: 0.19849851727485657
Epoch 10/10, Loss: 0.07784304330642852
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.61 1.00 0.76 11
Mines 0.74 0.89 0.81 19
Pipes 1.00 0.88 0.94 17
Rockets 0.91 0.91 0.91 11
Vehicles 1.00 0.25 0.40 12
accuracy 0.80 70
macro avg 0.85 0.79 0.76 70
weighted avg 0.85 0.80 0.78 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.4698099957572088
Epoch 2/10, Loss: 0.9005465805530548
Epoch 3/10, Loss: 0.49063285771343446
Epoch 4/10, Loss: 0.14928779006004333
Epoch 5/10, Loss: 0.2778902243201931
Epoch 6/10, Loss: 0.38487782660457825
Epoch 7/10, Loss: 0.14351868463887107
Epoch 8/10, Loss: 0.17914781615965897
Epoch 9/10, Loss: 0.12129095667559239
Epoch 10/10, Loss: 0.07515684696328309
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.73 0.84 11
Mines 0.80 0.84 0.82 19
Pipes 0.94 0.88 0.91 17
Rockets 0.71 0.91 0.80 11
Vehicles 0.92 0.92 0.92 12
accuracy 0.86 70
macro avg 0.87 0.86 0.86 70
weighted avg 0.87 0.86 0.86 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.4280984037452273
Epoch 2/10, Loss: 0.7566981348726485
Epoch 3/10, Loss: 0.4131372082564566
Epoch 4/10, Loss: 0.35493264264530605
Epoch 5/10, Loss: 0.2881416206558545
Epoch 6/10, Loss: 0.17963969350482026
Epoch 7/10, Loss: 0.16920633039747676
Epoch 8/10, Loss: 0.15839388904472193
Epoch 9/10, Loss: 0.06684846464648014
Epoch 10/10, Loss: 0.11177098879124969
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.94 0.84 0.89 19
Pipes 0.94 1.00 0.97 17
Rockets 0.75 0.82 0.78 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.87 70
macro avg 0.87 0.87 0.86 70
weighted avg 0.89 0.87 0.87 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.4571029709445105
Epoch 2/10, Loss: 0.8301633737153478
Epoch 3/10, Loss: 0.5103328915105926
Epoch 4/10, Loss: 0.3145267541209857
Epoch 5/10, Loss: 0.26324801291856503
Epoch 6/10, Loss: 0.17442356712288326
Epoch 7/10, Loss: 0.22983253276389506
Epoch 8/10, Loss: 0.15379521310225958
Epoch 9/10, Loss: 0.16828122610847154
Epoch 10/10, Loss: 0.09069780146496163
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.85 0.89 0.87 19
Pipes 0.89 0.94 0.91 17
Rockets 0.82 0.82 0.82 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.86 70
macro avg 0.87 0.85 0.85 70
weighted avg 0.87 0.86 0.86 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3813200427426233
Epoch 2/10, Loss: 0.7932123839855194
Epoch 3/10, Loss: 0.5566153468357192
Epoch 4/10, Loss: 0.31083059145344627
Epoch 5/10, Loss: 0.21072129988008076
Epoch 6/10, Loss: 0.20694315992295742
Epoch 7/10, Loss: 0.13196158150417936
Epoch 8/10, Loss: 0.08953369020794828
Epoch 9/10, Loss: 0.04392134274045626
Epoch 10/10, Loss: 0.20267192101002568
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.90 0.95 0.92 19
Pipes 0.88 0.88 0.88 17
Rockets 0.78 0.64 0.70 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.84 70
macro avg 0.83 0.83 0.82 70
weighted avg 0.84 0.84 0.84 70
Training efficientnet with lr=0.0005, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3744933042261336
Epoch 2/10, Loss: 0.6851638588640425
Epoch 3/10, Loss: 0.516158333255185
Epoch 4/10, Loss: 0.2829042143291897
Epoch 5/10, Loss: 0.18430107190377182
Epoch 6/10, Loss: 0.36417337134480476
Epoch 7/10, Loss: 0.16847707662317488
Epoch 8/10, Loss: 0.1062358170747757
Epoch 9/10, Loss: 0.08879458443779084
Epoch 10/10, Loss: 0.08122058709462483
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.89 0.89 0.89 19
Pipes 1.00 0.88 0.94 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.89 70
macro avg 0.88 0.89 0.88 70
weighted avg 0.89 0.89 0.89 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.3680725362565782
Epoch 2/10, Loss: 0.5997747845119901
Epoch 3/10, Loss: 0.22386960685253143
Epoch 4/10, Loss: 0.07760729847682847
Epoch 5/10, Loss: 0.04674274753779173
Epoch 6/10, Loss: 0.059980691721042
Epoch 7/10, Loss: 0.0646490964314176
Epoch 8/10, Loss: 0.04840940951059262
Epoch 9/10, Loss: 0.028442734490252204
Epoch 10/10, Loss: 0.031065571897973616
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.89 0.89 0.89 19
Pipes 0.94 0.94 0.94 17
Rockets 0.91 0.91 0.91 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.91 70
macro avg 0.91 0.92 0.91 70
weighted avg 0.91 0.91 0.91 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.3632600704828899
Epoch 2/10, Loss: 0.6057640810807546
Epoch 3/10, Loss: 0.2734196616543664
Epoch 4/10, Loss: 0.11092265860901938
Epoch 5/10, Loss: 0.04591191477245755
Epoch 6/10, Loss: 0.047827668539765805
Epoch 7/10, Loss: 0.07029655389487743
Epoch 8/10, Loss: 0.05044354068943196
Epoch 9/10, Loss: 0.07010280164993471
Epoch 10/10, Loss: 0.05819771676841709
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.82 0.95 0.88 19
Pipes 1.00 0.94 0.97 17
Rockets 0.92 1.00 0.96 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.91 70
macro avg 0.93 0.91 0.91 70
weighted avg 0.92 0.91 0.91 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.4015393919414945
Epoch 2/10, Loss: 0.7915562192598978
Epoch 3/10, Loss: 0.42063352796766496
Epoch 4/10, Loss: 0.14381683866182962
Epoch 5/10, Loss: 0.1190037073360549
Epoch 6/10, Loss: 0.08091474738385943
Epoch 7/10, Loss: 0.03919971754981412
Epoch 8/10, Loss: 0.03243319886840052
Epoch 9/10, Loss: 0.025047305453982618
Epoch 10/10, Loss: 0.02949133546402057
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 0.94 0.97 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.93 70
macro avg 0.92 0.93 0.92 70
weighted avg 0.93 0.93 0.93 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.2772464685969882
Epoch 2/10, Loss: 0.5058518913057115
Epoch 3/10, Loss: 0.192999461458789
Epoch 4/10, Loss: 0.09754151023096508
Epoch 5/10, Loss: 0.05228897163437472
Epoch 6/10, Loss: 0.030431212650405035
Epoch 7/10, Loss: 0.03944466050921215
Epoch 8/10, Loss: 0.0606567848008126
Epoch 9/10, Loss: 0.09882196825411585
Epoch 10/10, Loss: 0.09927669498655531
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.82 0.74 0.78 19
Pipes 0.94 0.88 0.91 17
Rockets 0.69 0.82 0.75 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.83 70
macro avg 0.83 0.84 0.83 70
weighted avg 0.84 0.83 0.83 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3334898948669434
Epoch 2/10, Loss: 0.6105149388313293
Epoch 3/10, Loss: 0.22876006530390847
Epoch 4/10, Loss: 0.10603955056932238
Epoch 5/10, Loss: 0.07797886824442281
Epoch 6/10, Loss: 0.05545002894683017
Epoch 7/10, Loss: 0.044260135437879294
Epoch 8/10, Loss: 0.028032943347675934
Epoch 9/10, Loss: 0.017238549060291715
Epoch 10/10, Loss: 0.03857855710925327
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.82 0.95 0.88 19
Pipes 1.00 0.88 0.94 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.90 70
macro avg 0.91 0.90 0.90 70
weighted avg 0.91 0.90 0.90 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.4413171079423692
Epoch 2/10, Loss: 0.7446685565842522
Epoch 3/10, Loss: 0.3364235758781433
Epoch 4/10, Loss: 0.1989703377087911
Epoch 5/10, Loss: 0.07470890093180868
Epoch 6/10, Loss: 0.09095997166716391
Epoch 7/10, Loss: 0.047649101043740906
Epoch 8/10, Loss: 0.045856044420765504
Epoch 9/10, Loss: 0.05140780129780372
Epoch 10/10, Loss: 0.04123577941209078
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 1.00 0.89 0.94 19
Pipes 1.00 0.94 0.97 17
Rockets 0.83 0.91 0.87 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.90 70
macro avg 0.90 0.90 0.89 70
weighted avg 0.92 0.90 0.90 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.324642499287923
Epoch 2/10, Loss: 0.5964669585227966
Epoch 3/10, Loss: 0.21811561038096747
Epoch 4/10, Loss: 0.10489342030551699
Epoch 5/10, Loss: 0.05136860782901446
Epoch 6/10, Loss: 0.04902210014147891
Epoch 7/10, Loss: 0.03710943729513221
Epoch 8/10, Loss: 0.027180794419513807
Epoch 9/10, Loss: 0.08799089832852285
Epoch 10/10, Loss: 0.08315521085427867
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.73 0.84 11
Mines 1.00 0.89 0.94 19
Pipes 0.94 0.94 0.94 17
Rockets 0.73 1.00 0.85 11
Vehicles 0.85 0.92 0.88 12
accuracy 0.90 70
macro avg 0.90 0.90 0.89 70
weighted avg 0.92 0.90 0.90 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.373479273584154
Epoch 2/10, Loss: 0.6501987973848978
Epoch 3/10, Loss: 0.26867565843794083
Epoch 4/10, Loss: 0.1402774312429958
Epoch 5/10, Loss: 0.09556673425767157
Epoch 6/10, Loss: 0.054205887433555394
Epoch 7/10, Loss: 0.0820286429176728
Epoch 8/10, Loss: 0.028742975658840604
Epoch 9/10, Loss: 0.045565975778218776
Epoch 10/10, Loss: 0.04530656311867966
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.90 0.95 0.92 19
Pipes 0.88 0.88 0.88 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.91 70
macro avg 0.93 0.91 0.92 70
weighted avg 0.92 0.91 0.91 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.366333497895135
Epoch 2/10, Loss: 0.7484351992607117
Epoch 3/10, Loss: 0.3561138841840956
Epoch 4/10, Loss: 0.1662895381450653
Epoch 5/10, Loss: 0.08353887084457609
Epoch 6/10, Loss: 0.07052506299482451
Epoch 7/10, Loss: 0.04441069376965364
Epoch 8/10, Loss: 0.04017378990021017
Epoch 9/10, Loss: 0.03011254697210259
Epoch 10/10, Loss: 0.04690764596064886
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.89 0.89 0.89 19
Pipes 0.74 0.82 0.78 17
Rockets 0.90 0.82 0.86 11
Vehicles 1.00 0.92 0.96 12
accuracy 0.87 70
macro avg 0.89 0.87 0.88 70
weighted avg 0.88 0.87 0.87 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.646829644838969
Epoch 2/10, Loss: 1.6183118555280898
Epoch 3/10, Loss: 1.5682004160351224
Epoch 4/10, Loss: 1.5066024329927232
Epoch 5/10, Loss: 1.4612599743737116
Epoch 6/10, Loss: 1.4066393507851496
Epoch 7/10, Loss: 1.3788528972201877
Epoch 8/10, Loss: 1.324613438712226
Epoch 9/10, Loss: 1.2734729448954265
Epoch 10/10, Loss: 1.2300540208816528
Accuracy: 55.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.57 0.73 0.64 11
Mines 0.54 0.68 0.60 19
Pipes 1.00 0.29 0.45 17
Rockets 0.50 0.36 0.42 11
Vehicles 0.47 0.75 0.58 12
accuracy 0.56 70
macro avg 0.62 0.56 0.54 70
weighted avg 0.64 0.56 0.54 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.635303841696845
Epoch 2/10, Loss: 1.5814130703608196
Epoch 3/10, Loss: 1.5246539645724826
Epoch 4/10, Loss: 1.4859264426761203
Epoch 5/10, Loss: 1.417287998729282
Epoch 6/10, Loss: 1.3902132908503215
Epoch 7/10, Loss: 1.3277991082933214
Epoch 8/10, Loss: 1.2944739659627278
Epoch 9/10, Loss: 1.2095910840564303
Epoch 10/10, Loss: 1.1782185501522489
Accuracy: 61.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.64 0.56 11
Mines 0.65 0.58 0.61 19
Pipes 0.91 0.59 0.71 17
Rockets 0.55 0.55 0.55 11
Vehicles 0.53 0.75 0.62 12
accuracy 0.61 70
macro avg 0.63 0.62 0.61 70
weighted avg 0.65 0.61 0.62 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.609292361471388
Epoch 2/10, Loss: 1.5536044041315715
Epoch 3/10, Loss: 1.5312864912880793
Epoch 4/10, Loss: 1.4645634359783597
Epoch 5/10, Loss: 1.4150181081559923
Epoch 6/10, Loss: 1.4149671792984009
Epoch 7/10, Loss: 1.339390794436137
Epoch 8/10, Loss: 1.3058026764127943
Epoch 9/10, Loss: 1.2254094945059881
Epoch 10/10, Loss: 1.1992090808020697
Accuracy: 55.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.38 0.82 0.51 11
Mines 0.56 0.53 0.54 19
Pipes 1.00 0.59 0.74 17
Rockets 0.40 0.36 0.38 11
Vehicles 0.75 0.50 0.60 12
accuracy 0.56 70
macro avg 0.62 0.56 0.56 70
weighted avg 0.64 0.56 0.57 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6248311599095662
Epoch 2/10, Loss: 1.5820747878816392
Epoch 3/10, Loss: 1.5264836814668443
Epoch 4/10, Loss: 1.4861100249820285
Epoch 5/10, Loss: 1.4185865587658353
Epoch 6/10, Loss: 1.3767113553153143
Epoch 7/10, Loss: 1.309253387980991
Epoch 8/10, Loss: 1.270572026570638
Epoch 9/10, Loss: 1.2194576130972967
Epoch 10/10, Loss: 1.1812211010191176
Accuracy: 64.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.64 0.56 11
Mines 0.71 0.63 0.67 19
Pipes 0.91 0.59 0.71 17
Rockets 0.46 0.55 0.50 11
Vehicles 0.67 0.83 0.74 12
accuracy 0.64 70
macro avg 0.65 0.65 0.64 70
weighted avg 0.68 0.64 0.65 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6278841760423448
Epoch 2/10, Loss: 1.5595344305038452
Epoch 3/10, Loss: 1.5339229371812608
Epoch 4/10, Loss: 1.4713504976696439
Epoch 5/10, Loss: 1.4498574998643663
Epoch 6/10, Loss: 1.3904110723071628
Epoch 7/10, Loss: 1.3443156083424885
Epoch 8/10, Loss: 1.2976220978630915
Epoch 9/10, Loss: 1.2722670634587605
Epoch 10/10, Loss: 1.2096881469090779
Accuracy: 45.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.30 0.55 0.39 11
Mines 0.64 0.37 0.47 19
Pipes 0.88 0.41 0.56 17
Rockets 0.18 0.18 0.18 11
Vehicles 0.50 0.83 0.62 12
accuracy 0.46 70
macro avg 0.50 0.47 0.44 70
weighted avg 0.55 0.46 0.46 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.659228006998698
Epoch 2/10, Loss: 1.6003973086675007
Epoch 3/10, Loss: 1.552340030670166
Epoch 4/10, Loss: 1.527369909816318
Epoch 5/10, Loss: 1.4829805427127414
Epoch 6/10, Loss: 1.422877894507514
Epoch 7/10, Loss: 1.3923955625957913
Epoch 8/10, Loss: 1.347176194190979
Epoch 9/10, Loss: 1.2918472952312894
Epoch 10/10, Loss: 1.217935840288798
Accuracy: 47.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.35 0.55 0.43 11
Mines 0.42 0.26 0.32 19
Pipes 1.00 0.41 0.58 17
Rockets 0.50 0.55 0.52 11
Vehicles 0.41 0.75 0.53 12
accuracy 0.47 70
macro avg 0.54 0.50 0.48 70
weighted avg 0.56 0.47 0.47 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6293492317199707
Epoch 2/10, Loss: 1.5823202927907307
Epoch 3/10, Loss: 1.5391119321187336
Epoch 4/10, Loss: 1.4887492391798232
Epoch 5/10, Loss: 1.4340692891014948
Epoch 6/10, Loss: 1.3895386589898004
Epoch 7/10, Loss: 1.3403986030154758
Epoch 8/10, Loss: 1.2929998238881428
Epoch 9/10, Loss: 1.2557772000630696
Epoch 10/10, Loss: 1.2157325744628906
Accuracy: 48.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.47 0.73 0.57 11
Mines 0.52 0.58 0.55 19
Pipes 1.00 0.35 0.52 17
Rockets 0.50 0.09 0.15 11
Vehicles 0.33 0.67 0.44 12
accuracy 0.49 70
macro avg 0.57 0.48 0.45 70
weighted avg 0.59 0.49 0.47 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6096077627605863
Epoch 2/10, Loss: 1.5650506416956584
Epoch 3/10, Loss: 1.5142987304263644
Epoch 4/10, Loss: 1.4656827052434285
Epoch 5/10, Loss: 1.421808573934767
Epoch 6/10, Loss: 1.373323678970337
Epoch 7/10, Loss: 1.3257907496558294
Epoch 8/10, Loss: 1.2686432070202298
Epoch 9/10, Loss: 1.2250678141911824
Epoch 10/10, Loss: 1.2052507400512695
Accuracy: 57.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.44 0.73 0.55 11
Mines 0.75 0.63 0.69 19
Pipes 1.00 0.41 0.58 17
Rockets 0.50 0.27 0.35 11
Vehicles 0.43 0.83 0.57 12
accuracy 0.57 70
macro avg 0.63 0.58 0.55 70
weighted avg 0.67 0.57 0.57 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.604270749621921
Epoch 2/10, Loss: 1.5894509421454535
Epoch 3/10, Loss: 1.539332628250122
Epoch 4/10, Loss: 1.490392221344842
Epoch 5/10, Loss: 1.4167159001032512
Epoch 6/10, Loss: 1.4007946517732408
Epoch 7/10, Loss: 1.3446560965643988
Epoch 8/10, Loss: 1.299754606352912
Epoch 9/10, Loss: 1.269920163684421
Epoch 10/10, Loss: 1.1963687936464946
Accuracy: 54.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.43 0.82 0.56 11
Mines 0.53 0.53 0.53 19
Pipes 1.00 0.59 0.74 17
Rockets 0.43 0.27 0.33 11
Vehicles 0.46 0.50 0.48 12
accuracy 0.54 70
macro avg 0.57 0.54 0.53 70
weighted avg 0.60 0.54 0.55 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.5442938672171698
Epoch 2/10, Loss: 0.9040185544225905
Epoch 3/10, Loss: 0.327551512254609
Epoch 4/10, Loss: 0.17388185775942272
Epoch 5/10, Loss: 0.18914725072681904
Epoch 6/10, Loss: 0.09641007934179571
Epoch 7/10, Loss: 0.2705987902979056
Epoch 8/10, Loss: 0.06341027903060119
Epoch 9/10, Loss: 0.10820386103457874
Epoch 10/10, Loss: 0.1482899124837584
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.75 0.95 0.84 19
Pipes 1.00 0.88 0.94 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.86 0.50 0.63 12
accuracy 0.86 70
macro avg 0.87 0.85 0.85 70
weighted avg 0.87 0.86 0.85 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.4803676075405545
Epoch 2/10, Loss: 0.8572736183802286
Epoch 3/10, Loss: 0.5204804870817397
Epoch 4/10, Loss: 0.29873132622904247
Epoch 5/10, Loss: 0.15822764237721762
Epoch 6/10, Loss: 0.10810759704973963
Epoch 7/10, Loss: 0.09625407391124302
Epoch 8/10, Loss: 0.07160901029904683
Epoch 9/10, Loss: 0.059622202379008137
Epoch 10/10, Loss: 0.0834766705520451
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.89 0.73 0.80 11
Mines 0.73 0.84 0.78 19
Pipes 1.00 0.94 0.97 17
Rockets 0.80 0.73 0.76 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.83 70
macro avg 0.84 0.81 0.82 70
weighted avg 0.84 0.83 0.83 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.4550205800268385
Epoch 2/10, Loss: 0.7752923154168658
Epoch 3/10, Loss: 0.33776727732684875
Epoch 4/10, Loss: 0.16844452006949318
Epoch 5/10, Loss: 0.261911245683829
Epoch 6/10, Loss: 0.08443155739870337
Epoch 7/10, Loss: 0.041309145796630115
Epoch 8/10, Loss: 0.17094824318256643
Epoch 9/10, Loss: 0.07215854422085816
Epoch 10/10, Loss: 0.013997546707590422
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.86 0.95 0.90 19
Pipes 1.00 0.94 0.97 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.91 70
macro avg 0.92 0.91 0.91 70
weighted avg 0.92 0.91 0.91 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5140246152877808
Epoch 2/10, Loss: 0.8879520561960008
Epoch 3/10, Loss: 0.3916401250494851
Epoch 4/10, Loss: 0.1933504417538643
Epoch 5/10, Loss: 0.14005578888787162
Epoch 6/10, Loss: 0.18897908470696873
Epoch 7/10, Loss: 0.16339335797561538
Epoch 8/10, Loss: 0.1428948152396414
Epoch 9/10, Loss: 0.041051611562983856
Epoch 10/10, Loss: 0.023251677138937846
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.82 0.95 0.88 19
Pipes 0.94 0.94 0.94 17
Rockets 0.91 0.91 0.91 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.91 70
macro avg 0.93 0.91 0.92 70
weighted avg 0.92 0.91 0.92 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.509302682346768
Epoch 2/10, Loss: 0.6871520247724321
Epoch 3/10, Loss: 0.405901453561253
Epoch 4/10, Loss: 0.33067410522037083
Epoch 5/10, Loss: 0.14202506384915775
Epoch 6/10, Loss: 0.06357628583080238
Epoch 7/10, Loss: 0.04265721276816395
Epoch 8/10, Loss: 0.07455416561828719
Epoch 9/10, Loss: 0.055635016308062606
Epoch 10/10, Loss: 0.028302804101258516
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.90 0.95 0.92 19
Pipes 0.93 0.82 0.88 17
Rockets 1.00 0.91 0.95 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.90 70
macro avg 0.91 0.90 0.90 70
weighted avg 0.91 0.90 0.90 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.4194065464867487
Epoch 2/10, Loss: 0.9092947840690613
Epoch 3/10, Loss: 0.2979233380821016
Epoch 4/10, Loss: 0.22125486863984
Epoch 5/10, Loss: 0.10903845437698895
Epoch 6/10, Loss: 0.0970233894056744
Epoch 7/10, Loss: 0.03345811253206597
Epoch 8/10, Loss: 0.12227970858414967
Epoch 9/10, Loss: 0.11776272476547295
Epoch 10/10, Loss: 0.03710572918256124
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.82 0.95 0.88 19
Pipes 1.00 0.88 0.94 17
Rockets 1.00 1.00 1.00 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.90 70
macro avg 0.92 0.90 0.90 70
weighted avg 0.92 0.90 0.90 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.4288432863023546
Epoch 2/10, Loss: 0.9826124442948235
Epoch 3/10, Loss: 0.5629340145323012
Epoch 4/10, Loss: 0.24392152163717482
Epoch 5/10, Loss: 0.2091399927934011
Epoch 6/10, Loss: 0.12365898531344202
Epoch 7/10, Loss: 0.08017263934016228
Epoch 8/10, Loss: 0.013711830083694723
Epoch 9/10, Loss: 0.06728426786139607
Epoch 10/10, Loss: 0.11062465582249893
Accuracy: 75.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.78 0.64 0.70 11
Mines 0.78 0.95 0.86 19
Pipes 0.64 0.82 0.72 17
Rockets 0.67 0.36 0.47 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.76 70
macro avg 0.77 0.72 0.73 70
weighted avg 0.77 0.76 0.75 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.4383954207102458
Epoch 2/10, Loss: 0.9336855676439073
Epoch 3/10, Loss: 0.4527189036210378
Epoch 4/10, Loss: 0.31887079609764946
Epoch 5/10, Loss: 0.14499622831741968
Epoch 6/10, Loss: 0.10123257090648015
Epoch 7/10, Loss: 0.05867375009175804
Epoch 8/10, Loss: 0.07252041674736473
Epoch 9/10, Loss: 0.0689005744126108
Epoch 10/10, Loss: 0.07618146327634652
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.82 0.95 0.88 19
Pipes 0.92 0.65 0.76 17
Rockets 0.77 0.91 0.83 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.86 70
macro avg 0.87 0.87 0.86 70
weighted avg 0.87 0.86 0.85 70
Training efficientnet with lr=0.0005, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.4986867507298787
Epoch 2/10, Loss: 0.7401817639668783
Epoch 3/10, Loss: 0.33117103907797074
Epoch 4/10, Loss: 0.28310610685083604
Epoch 5/10, Loss: 0.1089901373618179
Epoch 6/10, Loss: 0.11221202711264293
Epoch 7/10, Loss: 0.08334855590429571
Epoch 8/10, Loss: 0.09407060779631138
Epoch 9/10, Loss: 0.19510949816968706
Epoch 10/10, Loss: 0.1418608178695043
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.83 0.53 0.65 19
Pipes 0.71 1.00 0.83 17
Rockets 1.00 0.91 0.95 11
Vehicles 0.75 0.75 0.75 12
accuracy 0.81 70
macro avg 0.84 0.84 0.83 70
weighted avg 0.83 0.81 0.81 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.433146595954895
Epoch 2/10, Loss: 0.7750494003295898
Epoch 3/10, Loss: 0.386508446931839
Epoch 4/10, Loss: 0.15754468291997908
Epoch 5/10, Loss: 0.05487537682056427
Epoch 6/10, Loss: 0.028516852855682374
Epoch 7/10, Loss: 0.03947812020778656
Epoch 8/10, Loss: 0.015793485566973686
Epoch 9/10, Loss: 0.0337665596511215
Epoch 10/10, Loss: 0.0929668734781444
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.64 0.78 11
Mines 0.86 1.00 0.93 19
Pipes 0.83 0.88 0.86 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.86 70
macro avg 0.89 0.84 0.84 70
weighted avg 0.88 0.86 0.85 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.452770185470581
Epoch 2/10, Loss: 0.8243605136871338
Epoch 3/10, Loss: 0.39484637379646303
Epoch 4/10, Loss: 0.17357562333345414
Epoch 5/10, Loss: 0.08260160684585571
Epoch 6/10, Loss: 0.09423055946826935
Epoch 7/10, Loss: 0.03730705138295889
Epoch 8/10, Loss: 0.030903055518865585
Epoch 9/10, Loss: 0.03558751381933689
Epoch 10/10, Loss: 0.03429348133504391
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.61 1.00 0.76 11
Mines 0.88 0.74 0.80 19
Pipes 1.00 0.94 0.97 17
Rockets 0.91 0.91 0.91 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.86 70
macro avg 0.88 0.87 0.86 70
weighted avg 0.89 0.86 0.86 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.4884657859802246
Epoch 2/10, Loss: 0.9436404347419739
Epoch 3/10, Loss: 0.5752050280570984
Epoch 4/10, Loss: 0.32591046690940856
Epoch 5/10, Loss: 0.19434437453746795
Epoch 6/10, Loss: 0.09624805301427841
Epoch 7/10, Loss: 0.060149820148944856
Epoch 8/10, Loss: 0.037712656706571576
Epoch 9/10, Loss: 0.01899911779910326
Epoch 10/10, Loss: 0.03694028239697218
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 1.00 0.84 0.91 19
Pipes 1.00 0.94 0.97 17
Rockets 0.69 1.00 0.81 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.89 70
macro avg 0.89 0.89 0.88 70
weighted avg 0.91 0.89 0.89 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.4373699188232423
Epoch 2/10, Loss: 0.7477814435958863
Epoch 3/10, Loss: 0.3804622828960419
Epoch 4/10, Loss: 0.1505118265748024
Epoch 5/10, Loss: 0.05189978703856468
Epoch 6/10, Loss: 0.03466274365782738
Epoch 7/10, Loss: 0.03984801657497883
Epoch 8/10, Loss: 0.035863986052572724
Epoch 9/10, Loss: 0.0723665589466691
Epoch 10/10, Loss: 0.044433744996786116
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.85 0.89 0.87 19
Pipes 0.88 0.88 0.88 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.87 70
macro avg 0.88 0.87 0.87 70
weighted avg 0.88 0.87 0.87 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.4642487287521362
Epoch 2/10, Loss: 0.8126035809516907
Epoch 3/10, Loss: 0.428337436914444
Epoch 4/10, Loss: 0.19294363111257554
Epoch 5/10, Loss: 0.09321339875459671
Epoch 6/10, Loss: 0.05355499461293221
Epoch 7/10, Loss: 0.02901814356446266
Epoch 8/10, Loss: 0.018276096414774658
Epoch 9/10, Loss: 0.01629798598587513
Epoch 10/10, Loss: 0.03398904949426651
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.95 0.95 0.95 19
Pipes 0.94 1.00 0.97 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.93 70
macro avg 0.93 0.92 0.92 70
weighted avg 0.93 0.93 0.92 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.4990846872329713
Epoch 2/10, Loss: 0.9429099559783936
Epoch 3/10, Loss: 0.5332464575767517
Epoch 4/10, Loss: 0.30274999141693115
Epoch 5/10, Loss: 0.13754720166325568
Epoch 6/10, Loss: 0.11396333277225494
Epoch 7/10, Loss: 0.06590065211057664
Epoch 8/10, Loss: 0.0315057672560215
Epoch 9/10, Loss: 0.018981493636965753
Epoch 10/10, Loss: 0.021163370367139576
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.61 1.00 0.76 11
Mines 1.00 0.84 0.91 19
Pipes 1.00 0.94 0.97 17
Rockets 0.83 0.91 0.87 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.87 70
macro avg 0.89 0.87 0.86 70
weighted avg 0.91 0.87 0.88 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.4049360752105713
Epoch 2/10, Loss: 0.7406043529510498
Epoch 3/10, Loss: 0.34981575012207033
Epoch 4/10, Loss: 0.1399325594305992
Epoch 5/10, Loss: 0.05748425349593163
Epoch 6/10, Loss: 0.03664963617920876
Epoch 7/10, Loss: 0.09166443534195423
Epoch 8/10, Loss: 0.019195634033530952
Epoch 9/10, Loss: 0.03920841794461012
Epoch 10/10, Loss: 0.03624476119875908
Accuracy: 78.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.79 0.79 0.79 19
Pipes 0.81 0.76 0.79 17
Rockets 0.59 0.91 0.71 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.79 70
macro avg 0.82 0.79 0.79 70
weighted avg 0.82 0.79 0.79 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.443664836883545
Epoch 2/10, Loss: 0.8160343885421752
Epoch 3/10, Loss: 0.40334829688072205
Epoch 4/10, Loss: 0.16440751701593398
Epoch 5/10, Loss: 0.09991978853940964
Epoch 6/10, Loss: 0.032206153869628905
Epoch 7/10, Loss: 0.0700656209141016
Epoch 8/10, Loss: 0.0248412549495697
Epoch 9/10, Loss: 0.05554869286715984
Epoch 10/10, Loss: 0.07062445133924485
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 0.73 0.73 11
Mines 0.82 0.95 0.88 19
Pipes 1.00 0.88 0.94 17
Rockets 0.92 1.00 0.96 11
Vehicles 0.80 0.67 0.73 12
accuracy 0.86 70
macro avg 0.85 0.84 0.85 70
weighted avg 0.86 0.86 0.86 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.4661147832870483
Epoch 2/10, Loss: 0.8843893766403198
Epoch 3/10, Loss: 0.4919425666332245
Epoch 4/10, Loss: 0.2822397589683533
Epoch 5/10, Loss: 0.15458453744649886
Epoch 6/10, Loss: 0.0772977452725172
Epoch 7/10, Loss: 0.048157446831464765
Epoch 8/10, Loss: 0.06154668740928173
Epoch 9/10, Loss: 0.08755886442959308
Epoch 10/10, Loss: 0.05131440758705139
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.82 0.82 0.82 11
Mines 0.79 1.00 0.88 19
Pipes 0.89 0.94 0.91 17
Rockets 0.88 0.64 0.74 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.86 70
macro avg 0.87 0.83 0.84 70
weighted avg 0.87 0.86 0.85 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.5949363470077516
Epoch 2/10, Loss: 1.6049182891845704
Epoch 3/10, Loss: 1.5761444330215455
Epoch 4/10, Loss: 1.5354095697402954
Epoch 5/10, Loss: 1.5145708799362183
Epoch 6/10, Loss: 1.483395552635193
Epoch 7/10, Loss: 1.45811927318573
Epoch 8/10, Loss: 1.4148550510406495
Epoch 9/10, Loss: 1.3944012880325318
Epoch 10/10, Loss: 1.3593446969985963
Accuracy: 47.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.35 0.55 0.43 11
Mines 0.47 0.37 0.41 19
Pipes 0.60 0.35 0.44 17
Rockets 0.86 0.55 0.67 11
Vehicles 0.38 0.67 0.48 12
accuracy 0.47 70
macro avg 0.53 0.50 0.49 70
weighted avg 0.53 0.47 0.47 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6399336576461792
Epoch 2/10, Loss: 1.6221535205841064
Epoch 3/10, Loss: 1.5726490259170531
Epoch 4/10, Loss: 1.5518925428390502
Epoch 5/10, Loss: 1.5055730819702149
Epoch 6/10, Loss: 1.5075378656387328
Epoch 7/10, Loss: 1.487295389175415
Epoch 8/10, Loss: 1.4281502485275268
Epoch 9/10, Loss: 1.4437753677368164
Epoch 10/10, Loss: 1.3669759511947632
Accuracy: 38.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.40 0.36 0.38 11
Mines 0.56 0.26 0.36 19
Pipes 0.80 0.24 0.36 17
Rockets 0.28 0.45 0.34 11
Vehicles 0.32 0.75 0.45 12
accuracy 0.39 70
macro avg 0.47 0.41 0.38 70
weighted avg 0.51 0.39 0.38 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6114257335662843
Epoch 2/10, Loss: 1.6296860933303834
Epoch 3/10, Loss: 1.6085610866546631
Epoch 4/10, Loss: 1.5474161863327027
Epoch 5/10, Loss: 1.5244400262832642
Epoch 6/10, Loss: 1.517669129371643
Epoch 7/10, Loss: 1.4724538564682006
Epoch 8/10, Loss: 1.4406186580657958
Epoch 9/10, Loss: 1.4366759777069091
Epoch 10/10, Loss: 1.3816567182540893
Accuracy: 30.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.25 0.82 0.38 11
Mines 0.00 0.00 0.00 19
Pipes 0.80 0.24 0.36 17
Rockets 0.29 0.36 0.32 11
Vehicles 0.33 0.33 0.33 12
accuracy 0.30 70
macro avg 0.33 0.35 0.28 70
weighted avg 0.34 0.30 0.26 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6502938270568848
Epoch 2/10, Loss: 1.6003187894821167
Epoch 3/10, Loss: 1.570500421524048
Epoch 4/10, Loss: 1.5447251081466675
Epoch 5/10, Loss: 1.5094753742218017
Epoch 6/10, Loss: 1.4851518154144288
Epoch 7/10, Loss: 1.4613726615905762
Epoch 8/10, Loss: 1.4355324506759644
Epoch 9/10, Loss: 1.398714280128479
Epoch 10/10, Loss: 1.3696029663085938
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.39 0.64 0.48 11
Mines 0.42 0.58 0.49 19
Pipes 0.50 0.12 0.19 17
Rockets 0.14 0.18 0.16 11
Vehicles 0.50 0.33 0.40 12
accuracy 0.37 70
macro avg 0.39 0.37 0.34 70
weighted avg 0.41 0.37 0.35 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6375256538391114
Epoch 2/10, Loss: 1.602875781059265
Epoch 3/10, Loss: 1.6029693365097046
Epoch 4/10, Loss: 1.5690584182739258
Epoch 5/10, Loss: 1.5273188829421998
Epoch 6/10, Loss: 1.5003016233444213
Epoch 7/10, Loss: 1.4552608013153077
Epoch 8/10, Loss: 1.4221039533615112
Epoch 9/10, Loss: 1.408805012702942
Epoch 10/10, Loss: 1.366388201713562
Accuracy: 34.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.18 0.55 0.27 11
Mines 0.33 0.21 0.26 19
Pipes 0.75 0.35 0.48 17
Rockets 0.22 0.18 0.20 11
Vehicles 0.75 0.50 0.60 12
accuracy 0.34 70
macro avg 0.45 0.36 0.36 70
weighted avg 0.46 0.34 0.36 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6317105054855348
Epoch 2/10, Loss: 1.621117115020752
Epoch 3/10, Loss: 1.5994470596313477
Epoch 4/10, Loss: 1.560239028930664
Epoch 5/10, Loss: 1.5155452489852905
Epoch 6/10, Loss: 1.4933997869491578
Epoch 7/10, Loss: 1.4708372116088868
Epoch 8/10, Loss: 1.4423547744750977
Epoch 9/10, Loss: 1.4033344507217407
Epoch 10/10, Loss: 1.3816811084747314
Accuracy: 41.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.42 0.91 0.57 11
Mines 0.60 0.47 0.53 19
Pipes 1.00 0.12 0.21 17
Rockets 0.18 0.18 0.18 11
Vehicles 0.33 0.50 0.40 12
accuracy 0.41 70
macro avg 0.51 0.44 0.38 70
weighted avg 0.56 0.41 0.38 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6207947254180908
Epoch 2/10, Loss: 1.601033592224121
Epoch 3/10, Loss: 1.5732694864273071
Epoch 4/10, Loss: 1.5392633438110352
Epoch 5/10, Loss: 1.5185978889465332
Epoch 6/10, Loss: 1.4615644931793212
Epoch 7/10, Loss: 1.4519258260726928
Epoch 8/10, Loss: 1.421260952949524
Epoch 9/10, Loss: 1.394087052345276
Epoch 10/10, Loss: 1.3551345586776733
Accuracy: 41.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.37 0.64 0.47 11
Mines 0.33 0.32 0.32 19
Pipes 0.75 0.18 0.29 17
Rockets 0.67 0.18 0.29 11
Vehicles 0.42 0.92 0.58 12
accuracy 0.41 70
macro avg 0.51 0.45 0.39 70
weighted avg 0.51 0.41 0.37 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6318875312805177
Epoch 2/10, Loss: 1.6154306411743165
Epoch 3/10, Loss: 1.6176639556884767
Epoch 4/10, Loss: 1.5863160610198974
Epoch 5/10, Loss: 1.5182245254516602
Epoch 6/10, Loss: 1.5150922775268554
Epoch 7/10, Loss: 1.472383403778076
Epoch 8/10, Loss: 1.4487051486968994
Epoch 9/10, Loss: 1.4019833087921143
Epoch 10/10, Loss: 1.3803816795349122
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.26 0.45 0.33 11
Mines 0.00 0.00 0.00 19
Pipes 0.88 0.41 0.56 17
Rockets 0.27 0.36 0.31 11
Vehicles 0.48 0.83 0.61 12
accuracy 0.37 70
macro avg 0.38 0.41 0.36 70
weighted avg 0.38 0.37 0.34 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6387281894683838
Epoch 2/10, Loss: 1.5881093978881835
Epoch 3/10, Loss: 1.5756468057632447
Epoch 4/10, Loss: 1.5560747385025024
Epoch 5/10, Loss: 1.5208139657974242
Epoch 6/10, Loss: 1.4969375610351563
Epoch 7/10, Loss: 1.4615992069244386
Epoch 8/10, Loss: 1.4346413373947144
Epoch 9/10, Loss: 1.3951905012130736
Epoch 10/10, Loss: 1.379106616973877
Accuracy: 61.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.57 0.73 0.64 11
Mines 0.63 0.63 0.63 19
Pipes 0.83 0.59 0.69 17
Rockets 0.38 0.27 0.32 11
Vehicles 0.59 0.83 0.69 12
accuracy 0.61 70
macro avg 0.60 0.61 0.59 70
weighted avg 0.62 0.61 0.61 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.64884774684906
Epoch 2/10, Loss: 1.406836247444153
Epoch 3/10, Loss: 0.6549408674240113
Epoch 4/10, Loss: 0.36904520392417905
Epoch 5/10, Loss: 0.29827260673046113
Epoch 6/10, Loss: 0.39060371816158296
Epoch 7/10, Loss: 0.20964986383914946
Epoch 8/10, Loss: 0.06733786650002002
Epoch 9/10, Loss: 0.018814071267843246
Epoch 10/10, Loss: 0.014932287856936455
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 1.00 0.95 0.97 19
Pipes 1.00 0.88 0.94 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.91 70
macro avg 0.92 0.92 0.91 70
weighted avg 0.93 0.91 0.92 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.5779964447021484
Epoch 2/10, Loss: 0.9220783829689025
Epoch 3/10, Loss: 0.5692424714565277
Epoch 4/10, Loss: 0.2748609036207199
Epoch 5/10, Loss: 0.13100507557392121
Epoch 6/10, Loss: 0.13510844856500626
Epoch 7/10, Loss: 0.14669811278581618
Epoch 8/10, Loss: 0.06752099022269249
Epoch 9/10, Loss: 0.05198015086352825
Epoch 10/10, Loss: 0.031005704030394553
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.89 0.89 0.89 19
Pipes 0.88 0.88 0.88 17
Rockets 0.75 0.82 0.78 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.87 70
macro avg 0.87 0.87 0.87 70
weighted avg 0.88 0.87 0.87 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.5803441524505615
Epoch 2/10, Loss: 1.2709588050842284
Epoch 3/10, Loss: 0.6240016102790833
Epoch 4/10, Loss: 0.30344967544078827
Epoch 5/10, Loss: 0.17496336698532106
Epoch 6/10, Loss: 0.18516617119312287
Epoch 7/10, Loss: 0.07445184960961342
Epoch 8/10, Loss: 0.06469109617173671
Epoch 9/10, Loss: 0.03305590227246284
Epoch 10/10, Loss: 0.04583337716758251
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 0.89 0.84 0.86 19
Pipes 1.00 0.94 0.97 17
Rockets 1.00 0.82 0.90 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.89 70
macro avg 0.90 0.89 0.88 70
weighted avg 0.91 0.89 0.89 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5924517154693603
Epoch 2/10, Loss: 1.235628867149353
Epoch 3/10, Loss: 0.6656442284584045
Epoch 4/10, Loss: 0.3559752583503723
Epoch 5/10, Loss: 0.2730354964733124
Epoch 6/10, Loss: 0.133696748316288
Epoch 7/10, Loss: 0.0316819503903389
Epoch 8/10, Loss: 0.025678060948848724
Epoch 9/10, Loss: 0.04410295262932777
Epoch 10/10, Loss: 0.03151485174894333
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.85 0.89 0.87 19
Pipes 1.00 0.88 0.94 17
Rockets 0.83 0.91 0.87 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.87 70
macro avg 0.87 0.87 0.87 70
weighted avg 0.88 0.87 0.87 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3869422554969788
Epoch 2/10, Loss: 1.0499125599861145
Epoch 3/10, Loss: 0.4213641583919525
Epoch 4/10, Loss: 0.28863780200481415
Epoch 5/10, Loss: 0.09700792729854583
Epoch 6/10, Loss: 0.10394948422908783
Epoch 7/10, Loss: 0.05184808075428009
Epoch 8/10, Loss: 0.04892754722386598
Epoch 9/10, Loss: 0.01173393428325653
Epoch 10/10, Loss: 0.02658169250935316
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.83 1.00 0.90 19
Pipes 0.94 0.88 0.91 17
Rockets 0.92 1.00 0.96 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.90 70
macro avg 0.92 0.89 0.90 70
weighted avg 0.91 0.90 0.90 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5157134056091308
Epoch 2/10, Loss: 1.1431968212127686
Epoch 3/10, Loss: 0.49089098274707793
Epoch 4/10, Loss: 0.18655216842889785
Epoch 5/10, Loss: 0.1129706010222435
Epoch 6/10, Loss: 0.10901298969984055
Epoch 7/10, Loss: 0.051734726130962375
Epoch 8/10, Loss: 0.05886547602713108
Epoch 9/10, Loss: 0.01834479384124279
Epoch 10/10, Loss: 0.01239067129790783
Accuracy: 94.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 1.00 1.00 17
Rockets 0.92 1.00 0.96 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.94 70
macro avg 0.94 0.94 0.94 70
weighted avg 0.94 0.94 0.94 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.4951627254486084
Epoch 2/10, Loss: 1.0731887340545654
Epoch 3/10, Loss: 0.4944323003292084
Epoch 4/10, Loss: 0.19882022589445114
Epoch 5/10, Loss: 0.08389035016298294
Epoch 6/10, Loss: 0.11434917896986008
Epoch 7/10, Loss: 0.061632711067795755
Epoch 8/10, Loss: 0.033551538921892644
Epoch 9/10, Loss: 0.017524156346917154
Epoch 10/10, Loss: 0.0185901353135705
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.86 0.55 0.67 11
Mines 0.64 0.84 0.73 19
Pipes 1.00 0.82 0.90 17
Rockets 1.00 0.91 0.95 11
Vehicles 0.71 0.83 0.77 12
accuracy 0.80 70
macro avg 0.84 0.79 0.80 70
weighted avg 0.83 0.80 0.80 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.2674605131149292
Epoch 2/10, Loss: 1.0286479949951173
Epoch 3/10, Loss: 0.36230035722255705
Epoch 4/10, Loss: 0.1887925997376442
Epoch 5/10, Loss: 0.134196937084198
Epoch 6/10, Loss: 0.06994442790746688
Epoch 7/10, Loss: 0.04061499312520027
Epoch 8/10, Loss: 0.10957235768437386
Epoch 9/10, Loss: 0.04116362556815147
Epoch 10/10, Loss: 0.020089815743267535
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.86 1.00 0.93 19
Pipes 0.94 0.88 0.91 17
Rockets 0.91 0.91 0.91 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.90 70
macro avg 0.91 0.89 0.89 70
weighted avg 0.91 0.90 0.90 70
Training efficientnet with lr=0.0005, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3028064370155334
Epoch 2/10, Loss: 0.9506100058555603
Epoch 3/10, Loss: 0.4410764932632446
Epoch 4/10, Loss: 0.11646005362272263
Epoch 5/10, Loss: 0.09577328711748123
Epoch 6/10, Loss: 0.08404551818966866
Epoch 7/10, Loss: 0.1730421483516693
Epoch 8/10, Loss: 0.036106360517442225
Epoch 9/10, Loss: 0.06166893746703863
Epoch 10/10, Loss: 0.10210500434041023
Accuracy: 94.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.90 1.00 0.95 19
Pipes 1.00 0.94 0.97 17
Rockets 1.00 0.91 0.95 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.94 70
macro avg 0.95 0.94 0.94 70
weighted avg 0.95 0.94 0.94 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.5450266665882535
Epoch 2/10, Loss: 1.2295675741301642
Epoch 3/10, Loss: 0.9701528085602654
Epoch 4/10, Loss: 0.6908236742019653
Epoch 5/10, Loss: 0.530661188893848
Epoch 6/10, Loss: 0.32725248899724746
Epoch 7/10, Loss: 0.2650896633664767
Epoch 8/10, Loss: 0.20357414210836092
Epoch 9/10, Loss: 0.151614251650042
Epoch 10/10, Loss: 0.12008071194092433
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.90 0.95 0.92 19
Pipes 1.00 0.94 0.97 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.91 70
macro avg 0.92 0.91 0.91 70
weighted avg 0.92 0.91 0.91 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.5261686311827765
Epoch 2/10, Loss: 1.265367415216234
Epoch 3/10, Loss: 1.0053298771381378
Epoch 4/10, Loss: 0.766553110546536
Epoch 5/10, Loss: 0.5961940305100547
Epoch 6/10, Loss: 0.46222512589560616
Epoch 7/10, Loss: 0.35445936852031285
Epoch 8/10, Loss: 0.2632137897113959
Epoch 9/10, Loss: 0.1538793250090546
Epoch 10/10, Loss: 0.14774708325664201
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.90 0.95 0.92 19
Pipes 0.94 1.00 0.97 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.93 70
macro avg 0.94 0.92 0.92 70
weighted avg 0.94 0.93 0.93 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.538280102941725
Epoch 2/10, Loss: 1.2990880343649123
Epoch 3/10, Loss: 1.0651146007908716
Epoch 4/10, Loss: 0.8597059514787462
Epoch 5/10, Loss: 0.6574859917163849
Epoch 6/10, Loss: 0.526376497414377
Epoch 7/10, Loss: 0.42688559492429096
Epoch 8/10, Loss: 0.3163167130615976
Epoch 9/10, Loss: 0.24400457698437902
Epoch 10/10, Loss: 0.2687898994319969
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.86 0.95 0.90 19
Pipes 1.00 0.94 0.97 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.91 70
macro avg 0.93 0.91 0.91 70
weighted avg 0.93 0.91 0.92 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.485654956764645
Epoch 2/10, Loss: 1.157259765598509
Epoch 3/10, Loss: 0.9024163219663832
Epoch 4/10, Loss: 0.7056423359447055
Epoch 5/10, Loss: 0.5185913493235906
Epoch 6/10, Loss: 0.345621461669604
Epoch 7/10, Loss: 0.24682870672808754
Epoch 8/10, Loss: 0.150010510865185
Epoch 9/10, Loss: 0.14697912542356384
Epoch 10/10, Loss: 0.08843729262136751
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.86 0.95 0.90 19
Pipes 1.00 0.94 0.97 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.91 70
macro avg 0.93 0.91 0.91 70
weighted avg 0.93 0.91 0.91 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5160360667440627
Epoch 2/10, Loss: 1.2558018962542217
Epoch 3/10, Loss: 0.9705614513821073
Epoch 4/10, Loss: 0.769104626443651
Epoch 5/10, Loss: 0.5830114069912169
Epoch 6/10, Loss: 0.4462175865968068
Epoch 7/10, Loss: 0.30838562548160553
Epoch 8/10, Loss: 0.22073486571510634
Epoch 9/10, Loss: 0.17653429177072313
Epoch 10/10, Loss: 0.1425563299821483
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.90 0.95 0.92 19
Pipes 1.00 1.00 1.00 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.93 70
macro avg 0.93 0.92 0.92 70
weighted avg 0.93 0.93 0.93 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5958904955122206
Epoch 2/10, Loss: 1.359414067533281
Epoch 3/10, Loss: 1.1726075013478596
Epoch 4/10, Loss: 0.9815704425175985
Epoch 5/10, Loss: 0.7371496491962009
Epoch 6/10, Loss: 0.6067603280146917
Epoch 7/10, Loss: 0.44556312097443473
Epoch 8/10, Loss: 0.3267037338680691
Epoch 9/10, Loss: 0.24564457519186866
Epoch 10/10, Loss: 0.23833764096101126
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.85 1.00 0.92 11
Mines 0.89 0.89 0.89 19
Pipes 1.00 0.94 0.97 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.91 70
macro avg 0.92 0.92 0.91 70
weighted avg 0.92 0.91 0.91 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5026299622323778
Epoch 2/10, Loss: 1.1690727869669597
Epoch 3/10, Loss: 0.9062975545724233
Epoch 4/10, Loss: 0.65357639392217
Epoch 5/10, Loss: 0.5165523837010065
Epoch 6/10, Loss: 0.36014991170830196
Epoch 7/10, Loss: 0.2526445471578174
Epoch 8/10, Loss: 0.19857333848873773
Epoch 9/10, Loss: 0.12668827548623085
Epoch 10/10, Loss: 0.1151452221804195
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.84 0.84 0.84 19
Pipes 0.89 0.94 0.91 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.89 70
macro avg 0.90 0.89 0.89 70
weighted avg 0.89 0.89 0.89 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5497585733731587
Epoch 2/10, Loss: 1.2688978579309251
Epoch 3/10, Loss: 1.005079706509908
Epoch 4/10, Loss: 0.7860894964800941
Epoch 5/10, Loss: 0.6041586432192061
Epoch 6/10, Loss: 0.4344576762782203
Epoch 7/10, Loss: 0.29999244378672707
Epoch 8/10, Loss: 0.2157954995830854
Epoch 9/10, Loss: 0.14666940851344
Epoch 10/10, Loss: 0.13089441549446848
Accuracy: 94.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 1.00 1.00 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.94 70
macro avg 0.95 0.94 0.94 70
weighted avg 0.95 0.94 0.94 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5711979534890916
Epoch 2/10, Loss: 1.3290054268307157
Epoch 3/10, Loss: 1.1007127066453297
Epoch 4/10, Loss: 0.8841722243362002
Epoch 5/10, Loss: 0.7158050106631385
Epoch 6/10, Loss: 0.5821861889627244
Epoch 7/10, Loss: 0.40723134246137405
Epoch 8/10, Loss: 0.3105176231927342
Epoch 9/10, Loss: 0.27429810497495866
Epoch 10/10, Loss: 0.22981562051508161
Accuracy: 94.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 1.00 1.00 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.94 70
macro avg 0.95 0.94 0.94 70
weighted avg 0.95 0.94 0.94 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6142346991433039
Epoch 2/10, Loss: 1.5873228576448228
Epoch 3/10, Loss: 1.563508689403534
Epoch 4/10, Loss: 1.5468820267253451
Epoch 5/10, Loss: 1.5214055445459154
Epoch 6/10, Loss: 1.505240261554718
Epoch 7/10, Loss: 1.4931343793869019
Epoch 8/10, Loss: 1.47531259059906
Epoch 9/10, Loss: 1.4713551534546747
Epoch 10/10, Loss: 1.4418577286932204
Accuracy: 41.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.43 0.55 0.48 11
Mines 0.50 0.32 0.39 19
Pipes 0.75 0.18 0.29 17
Rockets 0.42 0.45 0.43 11
Vehicles 0.32 0.75 0.45 12
accuracy 0.41 70
macro avg 0.48 0.45 0.41 70
weighted avg 0.51 0.41 0.40 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6028516226344638
Epoch 2/10, Loss: 1.601859947045644
Epoch 3/10, Loss: 1.565879351562924
Epoch 4/10, Loss: 1.5692617893218994
Epoch 5/10, Loss: 1.5450114144219294
Epoch 6/10, Loss: 1.513522552119361
Epoch 7/10, Loss: 1.497616668542226
Epoch 8/10, Loss: 1.4821937481562297
Epoch 9/10, Loss: 1.4823228187031217
Epoch 10/10, Loss: 1.432850632402632
Accuracy: 48.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.58 0.64 0.61 11
Mines 0.71 0.26 0.38 19
Pipes 0.88 0.41 0.56 17
Rockets 0.50 0.27 0.35 11
Vehicles 0.32 1.00 0.49 12
accuracy 0.49 70
macro avg 0.60 0.52 0.48 70
weighted avg 0.63 0.49 0.48 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6194731328222487
Epoch 2/10, Loss: 1.6062102913856506
Epoch 3/10, Loss: 1.5762001143561468
Epoch 4/10, Loss: 1.5849645601378546
Epoch 5/10, Loss: 1.5415298541386921
Epoch 6/10, Loss: 1.5125068028767903
Epoch 7/10, Loss: 1.5033090909322102
Epoch 8/10, Loss: 1.474914312362671
Epoch 9/10, Loss: 1.4416685634189181
Epoch 10/10, Loss: 1.4377290672726102
Accuracy: 38.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.47 0.64 0.54 11
Mines 0.50 0.26 0.34 19
Pipes 0.67 0.12 0.20 17
Rockets 0.14 0.27 0.19 11
Vehicles 0.48 0.83 0.61 12
accuracy 0.39 70
macro avg 0.45 0.42 0.38 70
weighted avg 0.48 0.39 0.36 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6022230784098308
Epoch 2/10, Loss: 1.5898392664061651
Epoch 3/10, Loss: 1.557267917527093
Epoch 4/10, Loss: 1.5453579425811768
Epoch 5/10, Loss: 1.537181642320421
Epoch 6/10, Loss: 1.497494227356381
Epoch 7/10, Loss: 1.4852201739947002
Epoch 8/10, Loss: 1.4620063172446356
Epoch 9/10, Loss: 1.442577322324117
Epoch 10/10, Loss: 1.4312806593047247
Accuracy: 38.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.42 0.45 0.43 11
Mines 0.50 0.32 0.39 19
Pipes 0.50 0.24 0.32 17
Rockets 0.23 0.27 0.25 11
Vehicles 0.36 0.75 0.49 12
accuracy 0.39 70
macro avg 0.40 0.41 0.38 70
weighted avg 0.42 0.39 0.37 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.632956822713216
Epoch 2/10, Loss: 1.6137223111258612
Epoch 3/10, Loss: 1.5956673953268263
Epoch 4/10, Loss: 1.5524145894580417
Epoch 5/10, Loss: 1.5528465310732524
Epoch 6/10, Loss: 1.5369229647848341
Epoch 7/10, Loss: 1.526687502861023
Epoch 8/10, Loss: 1.4841032028198242
Epoch 9/10, Loss: 1.4706438448694017
Epoch 10/10, Loss: 1.464762442641788
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.29 0.36 0.32 11
Mines 0.29 0.21 0.24 19
Pipes 0.67 0.35 0.46 17
Rockets 0.19 0.27 0.22 11
Vehicles 0.53 0.75 0.62 12
accuracy 0.37 70
macro avg 0.39 0.39 0.37 70
weighted avg 0.40 0.37 0.37 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6587763163778517
Epoch 2/10, Loss: 1.6325865520371332
Epoch 3/10, Loss: 1.602735506163703
Epoch 4/10, Loss: 1.5878721872965496
Epoch 5/10, Loss: 1.5668295953008864
Epoch 6/10, Loss: 1.5698669685257807
Epoch 7/10, Loss: 1.5528928769959345
Epoch 8/10, Loss: 1.5346518953641255
Epoch 9/10, Loss: 1.524934834904141
Epoch 10/10, Loss: 1.4805187914106581
Accuracy: 41.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.54 0.64 0.58 11
Mines 0.53 0.42 0.47 19
Pipes 1.00 0.24 0.38 17
Rockets 0.23 0.27 0.25 11
Vehicles 0.28 0.58 0.38 12
accuracy 0.41 70
macro avg 0.52 0.43 0.41 70
weighted avg 0.56 0.41 0.42 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.627382927470737
Epoch 2/10, Loss: 1.6122946739196777
Epoch 3/10, Loss: 1.5780084729194641
Epoch 4/10, Loss: 1.5462478134367201
Epoch 5/10, Loss: 1.5288084414270189
Epoch 6/10, Loss: 1.5171061952908833
Epoch 7/10, Loss: 1.502955502933926
Epoch 8/10, Loss: 1.4784994257820978
Epoch 9/10, Loss: 1.474178632100423
Epoch 10/10, Loss: 1.4537756575478449
Accuracy: 42.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.20 0.45 0.28 11
Mines 0.64 0.47 0.55 19
Pipes 1.00 0.18 0.30 17
Rockets 0.43 0.27 0.33 11
Vehicles 0.48 0.83 0.61 12
accuracy 0.43 70
macro avg 0.55 0.44 0.41 70
weighted avg 0.60 0.43 0.42 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6530656549665663
Epoch 2/10, Loss: 1.640495200951894
Epoch 3/10, Loss: 1.6119554837544758
Epoch 4/10, Loss: 1.5994784368409052
Epoch 5/10, Loss: 1.557692077424791
Epoch 6/10, Loss: 1.56238677766588
Epoch 7/10, Loss: 1.497377547952864
Epoch 8/10, Loss: 1.4971630175908406
Epoch 9/10, Loss: 1.4824111726548936
Epoch 10/10, Loss: 1.4630228214793735
Accuracy: 31.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.32 0.55 0.40 11
Mines 0.30 0.16 0.21 19
Pipes 0.33 0.06 0.10 17
Rockets 0.29 0.36 0.32 11
Vehicles 0.33 0.67 0.44 12
accuracy 0.31 70
macro avg 0.31 0.36 0.29 70
weighted avg 0.31 0.31 0.27 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.661970595518748
Epoch 2/10, Loss: 1.6523077223036025
Epoch 3/10, Loss: 1.6297042700979445
Epoch 4/10, Loss: 1.5670634243223402
Epoch 5/10, Loss: 1.5497452682918973
Epoch 6/10, Loss: 1.538725389374627
Epoch 7/10, Loss: 1.5179584688610501
Epoch 8/10, Loss: 1.5045975314246283
Epoch 9/10, Loss: 1.4833784566985235
Epoch 10/10, Loss: 1.4619530902968512
Accuracy: 51.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.35 0.64 0.45 11
Mines 0.50 0.42 0.46 19
Pipes 0.82 0.53 0.64 17
Rockets 0.30 0.27 0.29 11
Vehicles 0.69 0.75 0.72 12
accuracy 0.51 70
macro avg 0.53 0.52 0.51 70
weighted avg 0.56 0.51 0.52 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.2191635800732508
Epoch 2/10, Loss: 0.6179631633891
Epoch 3/10, Loss: 0.3124809107846684
Epoch 4/10, Loss: 0.16743226597706476
Epoch 5/10, Loss: 0.12335207706524266
Epoch 6/10, Loss: 0.11081510554585192
Epoch 7/10, Loss: 0.08407428674399853
Epoch 8/10, Loss: 0.07079466251242492
Epoch 9/10, Loss: 0.07718857853776878
Epoch 10/10, Loss: 0.06505556921992037
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.94 0.89 0.92 19
Pipes 0.94 1.00 0.97 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.93 70
macro avg 0.93 0.93 0.93 70
weighted avg 0.93 0.93 0.93 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.2460496690538194
Epoch 2/10, Loss: 0.6335033360454772
Epoch 3/10, Loss: 0.3634566482570436
Epoch 4/10, Loss: 0.26056720937291783
Epoch 5/10, Loss: 0.212087611357371
Epoch 6/10, Loss: 0.16769326747291619
Epoch 7/10, Loss: 0.0994545386897193
Epoch 8/10, Loss: 0.08460089202142423
Epoch 9/10, Loss: 0.06090078999598821
Epoch 10/10, Loss: 0.06879128469154239
Accuracy: 94.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 1.00 1.00 17
Rockets 0.92 1.00 0.96 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.94 70
macro avg 0.94 0.94 0.94 70
weighted avg 0.94 0.94 0.94 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.3340352177619934
Epoch 2/10, Loss: 0.8265263670020633
Epoch 3/10, Loss: 0.5555034495062299
Epoch 4/10, Loss: 0.3870337696539031
Epoch 5/10, Loss: 0.21529046818614006
Epoch 6/10, Loss: 0.22608496993780136
Epoch 7/10, Loss: 0.17516215994126266
Epoch 8/10, Loss: 0.10965750076704556
Epoch 9/10, Loss: 0.09167404234823254
Epoch 10/10, Loss: 0.11938789414448871
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.84 0.84 0.84 19
Pipes 1.00 0.88 0.94 17
Rockets 0.79 1.00 0.88 11
Vehicles 0.77 0.83 0.80 12
accuracy 0.87 70
macro avg 0.88 0.88 0.87 70
weighted avg 0.88 0.87 0.87 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.1929154958989885
Epoch 2/10, Loss: 0.4627358383602566
Epoch 3/10, Loss: 0.30450483080413604
Epoch 4/10, Loss: 0.15659340802166197
Epoch 5/10, Loss: 0.1307975286617875
Epoch 6/10, Loss: 0.0921148431694342
Epoch 7/10, Loss: 0.09168133750143978
Epoch 8/10, Loss: 0.08600846610756384
Epoch 9/10, Loss: 0.08834443205139703
Epoch 10/10, Loss: 0.040106643508705825
Accuracy: 94.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.95 0.95 0.95 19
Pipes 0.94 0.94 0.94 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.92 0.96 12
accuracy 0.94 70
macro avg 0.95 0.94 0.94 70
weighted avg 0.95 0.94 0.94 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.2166430089208815
Epoch 2/10, Loss: 0.6339521706104279
Epoch 3/10, Loss: 0.3887210645609432
Epoch 4/10, Loss: 0.2938441721101602
Epoch 5/10, Loss: 0.2157439072098997
Epoch 6/10, Loss: 0.14234597836103705
Epoch 7/10, Loss: 0.08972783997240993
Epoch 8/10, Loss: 0.08121172204199764
Epoch 9/10, Loss: 0.061880523080213204
Epoch 10/10, Loss: 0.08959098641450207
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.90 0.95 0.92 19
Pipes 1.00 1.00 1.00 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.93 70
macro avg 0.94 0.92 0.92 70
weighted avg 0.94 0.93 0.93 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.309949927859836
Epoch 2/10, Loss: 0.7491819130049812
Epoch 3/10, Loss: 0.4676171276304457
Epoch 4/10, Loss: 0.3037010431289673
Epoch 5/10, Loss: 0.2254115388625198
Epoch 6/10, Loss: 0.16896315415700278
Epoch 7/10, Loss: 0.13179898944993815
Epoch 8/10, Loss: 0.13579741068598297
Epoch 9/10, Loss: 0.09267923473897907
Epoch 10/10, Loss: 0.08842019891987245
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.85 0.89 0.87 19
Pipes 1.00 0.94 0.97 17
Rockets 0.85 1.00 0.92 11
Vehicles 0.90 0.75 0.82 12
accuracy 0.90 70
macro avg 0.90 0.90 0.90 70
weighted avg 0.90 0.90 0.90 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.1864639520645142
Epoch 2/10, Loss: 0.5517238908343844
Epoch 3/10, Loss: 0.29105895426538253
Epoch 4/10, Loss: 0.19798212415642208
Epoch 5/10, Loss: 0.1690523804475864
Epoch 6/10, Loss: 0.137766743492749
Epoch 7/10, Loss: 0.11686839898013407
Epoch 8/10, Loss: 0.08024628543191487
Epoch 9/10, Loss: 0.16262830183323887
Epoch 10/10, Loss: 0.07010995451774862
Accuracy: 95.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.95 1.00 0.97 19
Pipes 1.00 1.00 1.00 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.96 70
macro avg 0.96 0.95 0.95 70
weighted avg 0.96 0.96 0.96 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.2492289476924472
Epoch 2/10, Loss: 0.6305668933524026
Epoch 3/10, Loss: 0.3650063176949819
Epoch 4/10, Loss: 0.2479668137513929
Epoch 5/10, Loss: 0.16532081531153786
Epoch 6/10, Loss: 0.17172052007582453
Epoch 7/10, Loss: 0.08429813581622309
Epoch 8/10, Loss: 0.10016499718444215
Epoch 9/10, Loss: 0.06824245676398277
Epoch 10/10, Loss: 0.044568241806700826
Accuracy: 95.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 1.00 1.00 11
Mines 0.90 1.00 0.95 19
Pipes 1.00 0.94 0.97 17
Rockets 0.92 1.00 0.96 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.96 70
macro avg 0.96 0.95 0.96 70
weighted avg 0.96 0.96 0.96 70
Training efficientnet with lr=0.0001, batch_size=16, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3343946072790358
Epoch 2/10, Loss: 0.7640632821453942
Epoch 3/10, Loss: 0.4525776356458664
Epoch 4/10, Loss: 0.2912949191199409
Epoch 5/10, Loss: 0.22392539555827776
Epoch 6/10, Loss: 0.14070266609390578
Epoch 7/10, Loss: 0.11468741194241577
Epoch 8/10, Loss: 0.11838666949835089
Epoch 9/10, Loss: 0.05814908631145954
Epoch 10/10, Loss: 0.06071753453256355
Accuracy: 94.29%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.95 1.00 0.97 19
Pipes 1.00 1.00 1.00 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.94 70
macro avg 0.95 0.93 0.93 70
weighted avg 0.95 0.94 0.94 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.5755093495051067
Epoch 2/10, Loss: 1.2921920352511935
Epoch 3/10, Loss: 1.0956379373868306
Epoch 4/10, Loss: 0.8785400258170234
Epoch 5/10, Loss: 0.7310709158579508
Epoch 6/10, Loss: 0.5552553799417284
Epoch 7/10, Loss: 0.41068541010220844
Epoch 8/10, Loss: 0.3326918813917372
Epoch 9/10, Loss: 0.24627798133426243
Epoch 10/10, Loss: 0.17822065618303087
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.90 0.95 0.92 19
Pipes 1.00 0.94 0.97 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.93 70
macro avg 0.93 0.93 0.93 70
weighted avg 0.93 0.93 0.93 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.530696948369344
Epoch 2/10, Loss: 1.3240572214126587
Epoch 3/10, Loss: 1.0996331241395738
Epoch 4/10, Loss: 0.8739283813370599
Epoch 5/10, Loss: 0.7572632431983948
Epoch 6/10, Loss: 0.5770383808347914
Epoch 7/10, Loss: 0.4544655515087975
Epoch 8/10, Loss: 0.37651442488034564
Epoch 9/10, Loss: 0.271337714460161
Epoch 10/10, Loss: 0.215688180592325
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.94 0.89 0.92 19
Pipes 1.00 0.88 0.94 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.90 70
macro avg 0.90 0.90 0.90 70
weighted avg 0.92 0.90 0.90 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.5387012163798015
Epoch 2/10, Loss: 1.3394985066519842
Epoch 3/10, Loss: 1.1784430609809027
Epoch 4/10, Loss: 0.9986644784609476
Epoch 5/10, Loss: 0.8508086138301425
Epoch 6/10, Loss: 0.7162194119559394
Epoch 7/10, Loss: 0.5727808773517609
Epoch 8/10, Loss: 0.44591356648339164
Epoch 9/10, Loss: 0.38536157541804844
Epoch 10/10, Loss: 0.2926836245589786
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.89 0.89 0.89 19
Pipes 1.00 0.88 0.94 17
Rockets 0.71 0.91 0.80 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.86 70
macro avg 0.86 0.85 0.85 70
weighted avg 0.88 0.86 0.86 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5447955131530762
Epoch 2/10, Loss: 1.2477455271614923
Epoch 3/10, Loss: 1.0391716096136305
Epoch 4/10, Loss: 0.8446073200967577
Epoch 5/10, Loss: 0.6682582166459825
Epoch 6/10, Loss: 0.5229514274332259
Epoch 7/10, Loss: 0.37246951791975236
Epoch 8/10, Loss: 0.27646226518683964
Epoch 9/10, Loss: 0.22055795126491123
Epoch 10/10, Loss: 0.15194687495628992
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.94 0.89 0.92 19
Pipes 1.00 0.88 0.94 17
Rockets 0.79 1.00 0.88 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.87 70
macro avg 0.87 0.87 0.86 70
weighted avg 0.89 0.87 0.87 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.535911003748576
Epoch 2/10, Loss: 1.307514362865024
Epoch 3/10, Loss: 1.092789351940155
Epoch 4/10, Loss: 0.9530434476004707
Epoch 5/10, Loss: 0.7655488782458835
Epoch 6/10, Loss: 0.583052416642507
Epoch 7/10, Loss: 0.4661614067024655
Epoch 8/10, Loss: 0.3607328732808431
Epoch 9/10, Loss: 0.2511752165026135
Epoch 10/10, Loss: 0.1925237907303704
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.89 0.89 0.89 19
Pipes 1.00 0.88 0.94 17
Rockets 0.69 1.00 0.81 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.89 70
macro avg 0.90 0.89 0.88 70
weighted avg 0.91 0.89 0.89 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5631938113106623
Epoch 2/10, Loss: 1.353941520055135
Epoch 3/10, Loss: 1.1631143225563898
Epoch 4/10, Loss: 0.9861496422025893
Epoch 5/10, Loss: 0.8236378166410658
Epoch 6/10, Loss: 0.6795151498582628
Epoch 7/10, Loss: 0.5363637440734439
Epoch 8/10, Loss: 0.41881267229715985
Epoch 9/10, Loss: 0.38089707493782043
Epoch 10/10, Loss: 0.2663583656152089
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.85 0.89 0.87 19
Pipes 1.00 0.88 0.94 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.87 70
macro avg 0.88 0.87 0.86 70
weighted avg 0.89 0.87 0.87 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5506546629799738
Epoch 2/10, Loss: 1.2586926486757066
Epoch 3/10, Loss: 1.033745739195082
Epoch 4/10, Loss: 0.8545448581377665
Epoch 5/10, Loss: 0.6378932992617289
Epoch 6/10, Loss: 0.4622075491481357
Epoch 7/10, Loss: 0.33838516142633224
Epoch 8/10, Loss: 0.2661701871289147
Epoch 9/10, Loss: 0.18779667715231577
Epoch 10/10, Loss: 0.14033326506614685
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.94 0.89 0.92 19
Pipes 1.00 0.94 0.97 17
Rockets 0.79 1.00 0.88 11
Vehicles 0.91 0.83 0.87 12
accuracy 0.91 70
macro avg 0.91 0.92 0.91 70
weighted avg 0.92 0.91 0.92 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5418428977330525
Epoch 2/10, Loss: 1.3077202770445082
Epoch 3/10, Loss: 1.0912783874405756
Epoch 4/10, Loss: 0.9123718937238058
Epoch 5/10, Loss: 0.7835168970955743
Epoch 6/10, Loss: 0.6337628695699904
Epoch 7/10, Loss: 0.5117445455657111
Epoch 8/10, Loss: 0.40219634109073216
Epoch 9/10, Loss: 0.312196660372946
Epoch 10/10, Loss: 0.24862882163789538
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.88 0.79 0.83 19
Pipes 0.94 0.88 0.91 17
Rockets 0.71 0.91 0.80 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.83 70
macro avg 0.84 0.83 0.82 70
weighted avg 0.86 0.83 0.83 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5709096988042195
Epoch 2/10, Loss: 1.3620838854047987
Epoch 3/10, Loss: 1.1828164789411757
Epoch 4/10, Loss: 1.054226729604933
Epoch 5/10, Loss: 0.9023651679356893
Epoch 6/10, Loss: 0.7455476787355211
Epoch 7/10, Loss: 0.6222878694534302
Epoch 8/10, Loss: 0.5283232861095004
Epoch 9/10, Loss: 0.3878506090905931
Epoch 10/10, Loss: 0.32873061299324036
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.85 0.89 0.87 19
Pipes 1.00 0.88 0.94 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.90 70
macro avg 0.92 0.90 0.90 70
weighted avg 0.92 0.90 0.90 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.606126281950209
Epoch 2/10, Loss: 1.617586824629042
Epoch 3/10, Loss: 1.5881768067677815
Epoch 4/10, Loss: 1.5760934617784288
Epoch 5/10, Loss: 1.5663487248950534
Epoch 6/10, Loss: 1.5465073585510254
Epoch 7/10, Loss: 1.5351451237996419
Epoch 8/10, Loss: 1.5329022937350802
Epoch 9/10, Loss: 1.52555251121521
Epoch 10/10, Loss: 1.5115588638517592
Accuracy: 32.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.33 0.45 0.38 11
Mines 0.21 0.16 0.18 19
Pipes 0.54 0.41 0.47 17
Rockets 0.18 0.18 0.18 11
Vehicles 0.35 0.50 0.41 12
accuracy 0.33 70
macro avg 0.32 0.34 0.33 70
weighted avg 0.33 0.33 0.32 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6437757545047336
Epoch 2/10, Loss: 1.594092885653178
Epoch 3/10, Loss: 1.5872598886489868
Epoch 4/10, Loss: 1.5640001164542303
Epoch 5/10, Loss: 1.5534886254204645
Epoch 6/10, Loss: 1.5476668145921495
Epoch 7/10, Loss: 1.5295298761791654
Epoch 8/10, Loss: 1.515778448846605
Epoch 9/10, Loss: 1.5208202468024359
Epoch 10/10, Loss: 1.5064237780041165
Accuracy: 28.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.19 0.27 0.22 11
Mines 0.17 0.11 0.13 19
Pipes 0.56 0.29 0.38 17
Rockets 0.00 0.00 0.00 11
Vehicles 0.37 0.83 0.51 12
accuracy 0.29 70
macro avg 0.26 0.30 0.25 70
weighted avg 0.27 0.29 0.25 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6482169760598078
Epoch 2/10, Loss: 1.64798727300432
Epoch 3/10, Loss: 1.6302615801493328
Epoch 4/10, Loss: 1.6258520020378961
Epoch 5/10, Loss: 1.586385554737515
Epoch 6/10, Loss: 1.5713878207736545
Epoch 7/10, Loss: 1.5524154636594985
Epoch 8/10, Loss: 1.5519360038969252
Epoch 9/10, Loss: 1.5425456762313843
Epoch 10/10, Loss: 1.5258451965120103
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.25 0.45 0.32 11
Mines 0.67 0.32 0.43 19
Pipes 0.71 0.29 0.42 17
Rockets 0.33 0.45 0.38 11
Vehicles 0.26 0.42 0.32 12
accuracy 0.37 70
macro avg 0.45 0.39 0.38 70
weighted avg 0.49 0.37 0.38 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6110188298755221
Epoch 2/10, Loss: 1.6065696610344782
Epoch 3/10, Loss: 1.5864651997884114
Epoch 4/10, Loss: 1.565998911857605
Epoch 5/10, Loss: 1.547502703136868
Epoch 6/10, Loss: 1.5426321824391682
Epoch 7/10, Loss: 1.5175301631291707
Epoch 8/10, Loss: 1.5153643157747057
Epoch 9/10, Loss: 1.5079178015391033
Epoch 10/10, Loss: 1.4926633834838867
Accuracy: 34.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.42 0.45 0.43 11
Mines 0.36 0.42 0.39 19
Pipes 0.50 0.18 0.26 17
Rockets 0.36 0.36 0.36 11
Vehicles 0.21 0.33 0.26 12
accuracy 0.34 70
macro avg 0.37 0.35 0.34 70
weighted avg 0.38 0.34 0.34 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.644285864300198
Epoch 2/10, Loss: 1.6399067110485501
Epoch 3/10, Loss: 1.6291448672612507
Epoch 4/10, Loss: 1.6223070356580946
Epoch 5/10, Loss: 1.6031692557864718
Epoch 6/10, Loss: 1.5927608145607843
Epoch 7/10, Loss: 1.571525428030226
Epoch 8/10, Loss: 1.5757505761252508
Epoch 9/10, Loss: 1.562178333600362
Epoch 10/10, Loss: 1.5441961818271213
Accuracy: 38.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.43 0.27 0.33 11
Mines 0.41 0.47 0.44 19
Pipes 0.75 0.18 0.29 17
Rockets 0.29 0.55 0.38 11
Vehicles 0.38 0.50 0.43 12
accuracy 0.39 70
macro avg 0.45 0.39 0.37 70
weighted avg 0.47 0.39 0.37 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6496340566211276
Epoch 2/10, Loss: 1.6278775135676067
Epoch 3/10, Loss: 1.6247080432044134
Epoch 4/10, Loss: 1.5861953496932983
Epoch 5/10, Loss: 1.6100295649634466
Epoch 6/10, Loss: 1.578461766242981
Epoch 7/10, Loss: 1.5894235107633803
Epoch 8/10, Loss: 1.548656079504225
Epoch 9/10, Loss: 1.5327087110943265
Epoch 10/10, Loss: 1.5096320311228435
Accuracy: 32.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.20 0.09 0.12 11
Mines 0.33 0.58 0.42 19
Pipes 0.67 0.12 0.20 17
Rockets 0.29 0.18 0.22 11
Vehicles 0.32 0.58 0.41 12
accuracy 0.33 70
macro avg 0.36 0.31 0.28 70
weighted avg 0.38 0.33 0.29 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6427320904201932
Epoch 2/10, Loss: 1.6297041840023465
Epoch 3/10, Loss: 1.6003288957807753
Epoch 4/10, Loss: 1.5874643193350897
Epoch 5/10, Loss: 1.5665001736746893
Epoch 6/10, Loss: 1.5611071321699355
Epoch 7/10, Loss: 1.551847881740994
Epoch 8/10, Loss: 1.5306031836403742
Epoch 9/10, Loss: 1.517315944035848
Epoch 10/10, Loss: 1.5092311567730374
Accuracy: 32.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.25 0.55 0.34 11
Mines 0.50 0.26 0.34 19
Pipes 0.67 0.35 0.46 17
Rockets 0.17 0.09 0.12 11
Vehicles 0.24 0.42 0.30 12
accuracy 0.33 70
macro avg 0.36 0.33 0.31 70
weighted avg 0.40 0.33 0.33 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.6148422559102376
Epoch 2/10, Loss: 1.6028859217961628
Epoch 3/10, Loss: 1.5826041433546278
Epoch 4/10, Loss: 1.6015100876490276
Epoch 5/10, Loss: 1.5817111598120794
Epoch 6/10, Loss: 1.5583222972022162
Epoch 7/10, Loss: 1.5565719736946955
Epoch 8/10, Loss: 1.5501390960481432
Epoch 9/10, Loss: 1.5291904211044312
Epoch 10/10, Loss: 1.5018427636888292
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.22 0.18 0.20 11
Mines 0.24 0.21 0.22 19
Pipes 1.00 0.06 0.11 17
Rockets 0.17 0.18 0.17 11
Vehicles 0.26 0.67 0.37 12
accuracy 0.24 70
macro avg 0.38 0.26 0.22 70
weighted avg 0.41 0.24 0.21 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.62669571240743
Epoch 2/10, Loss: 1.6145834657880995
Epoch 3/10, Loss: 1.624769263797336
Epoch 4/10, Loss: 1.5939166413413153
Epoch 5/10, Loss: 1.5946022934383817
Epoch 6/10, Loss: 1.575573656294081
Epoch 7/10, Loss: 1.584138380156623
Epoch 8/10, Loss: 1.5563684039645724
Epoch 9/10, Loss: 1.5179305871327717
Epoch 10/10, Loss: 1.5226696067386203
Accuracy: 51.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.23 0.27 0.25 11
Mines 0.52 0.63 0.57 19
Pipes 0.83 0.59 0.69 17
Rockets 0.50 0.55 0.52 11
Vehicles 0.50 0.42 0.45 12
accuracy 0.51 70
macro avg 0.52 0.49 0.50 70
weighted avg 0.54 0.51 0.52 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.239868488576677
Epoch 2/10, Loss: 0.5768448876010047
Epoch 3/10, Loss: 0.29012251562542385
Epoch 4/10, Loss: 0.1609099581837654
Epoch 5/10, Loss: 0.08721700972980923
Epoch 6/10, Loss: 0.058302110268010035
Epoch 7/10, Loss: 0.057183068038688764
Epoch 8/10, Loss: 0.034727459566460714
Epoch 9/10, Loss: 0.04190857522189617
Epoch 10/10, Loss: 0.053207930384410754
Accuracy: 87.14%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.73 0.84 11
Mines 0.85 0.89 0.87 19
Pipes 1.00 0.94 0.97 17
Rockets 0.65 1.00 0.79 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.87 70
macro avg 0.90 0.86 0.87 70
weighted avg 0.90 0.87 0.87 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.302559985054864
Epoch 2/10, Loss: 0.6623205608791776
Epoch 3/10, Loss: 0.32747197482321
Epoch 4/10, Loss: 0.20974930127461752
Epoch 5/10, Loss: 0.1455492658747567
Epoch 6/10, Loss: 0.09777497376004855
Epoch 7/10, Loss: 0.0914861328072018
Epoch 8/10, Loss: 0.06394208388196097
Epoch 9/10, Loss: 0.044061422554983035
Epoch 10/10, Loss: 0.03376968867248959
Accuracy: 94.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.92 1.00 0.96 11
Mines 0.95 0.95 0.95 19
Pipes 0.94 1.00 0.97 17
Rockets 0.92 1.00 0.96 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.94 70
macro avg 0.95 0.94 0.94 70
weighted avg 0.95 0.94 0.94 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.3424075974358454
Epoch 2/10, Loss: 0.7837462226549784
Epoch 3/10, Loss: 0.48172662324375576
Epoch 4/10, Loss: 0.292945792277654
Epoch 5/10, Loss: 0.1820741883582539
Epoch 6/10, Loss: 0.1946658409304089
Epoch 7/10, Loss: 0.07365702879097727
Epoch 8/10, Loss: 0.0886704640256034
Epoch 9/10, Loss: 0.061306924662656255
Epoch 10/10, Loss: 0.05890041093031565
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 0.94 0.97 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.91 70
macro avg 0.91 0.91 0.90 70
weighted avg 0.93 0.91 0.91 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.1945618854628668
Epoch 2/10, Loss: 0.5271041227711571
Epoch 3/10, Loss: 0.2674422280655967
Epoch 4/10, Loss: 0.13975408259365293
Epoch 5/10, Loss: 0.11020112451579836
Epoch 6/10, Loss: 0.0889299607111348
Epoch 7/10, Loss: 0.07110686517424053
Epoch 8/10, Loss: 0.0701063693397575
Epoch 9/10, Loss: 0.03305275893459717
Epoch 10/10, Loss: 0.024809971037838194
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.86 0.95 0.90 19
Pipes 1.00 0.88 0.94 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.91 70
macro avg 0.93 0.91 0.92 70
weighted avg 0.93 0.91 0.92 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3149957723087735
Epoch 2/10, Loss: 0.6502329409122467
Epoch 3/10, Loss: 0.3281001266505983
Epoch 4/10, Loss: 0.18340369727876452
Epoch 5/10, Loss: 0.10787675322757827
Epoch 6/10, Loss: 0.07015363499522209
Epoch 7/10, Loss: 0.057443450308508344
Epoch 8/10, Loss: 0.049688222714596324
Epoch 9/10, Loss: 0.04880311857495043
Epoch 10/10, Loss: 0.04477714385009474
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.90 0.95 0.92 19
Pipes 1.00 0.88 0.94 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.91 70
macro avg 0.93 0.91 0.91 70
weighted avg 0.93 0.91 0.92 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3450432684686449
Epoch 2/10, Loss: 0.7187975645065308
Epoch 3/10, Loss: 0.45450063546498615
Epoch 4/10, Loss: 0.2712324476904339
Epoch 5/10, Loss: 0.20196937604082954
Epoch 6/10, Loss: 0.1135740222202407
Epoch 7/10, Loss: 0.10024622041318151
Epoch 8/10, Loss: 0.07348120543691847
Epoch 9/10, Loss: 0.08078633869687717
Epoch 10/10, Loss: 0.03967541808055507
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.90 0.95 0.92 19
Pipes 1.00 0.88 0.94 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.91 70
macro avg 0.92 0.91 0.91 70
weighted avg 0.92 0.91 0.92 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.2134467628267076
Epoch 2/10, Loss: 0.5576713913016849
Epoch 3/10, Loss: 0.31630408267180127
Epoch 4/10, Loss: 0.2155857053067949
Epoch 5/10, Loss: 0.14007813897397783
Epoch 6/10, Loss: 0.0965624459915691
Epoch 7/10, Loss: 0.0566610230339898
Epoch 8/10, Loss: 0.04324250523414877
Epoch 9/10, Loss: 0.04462925117048952
Epoch 10/10, Loss: 0.035427401256230145
Accuracy: 94.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.91 0.91 0.91 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 1.00 1.00 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.94 70
macro avg 0.94 0.94 0.94 70
weighted avg 0.95 0.94 0.94 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.274410863717397
Epoch 2/10, Loss: 0.6572095817989774
Epoch 3/10, Loss: 0.3821475605169932
Epoch 4/10, Loss: 0.22975500093566048
Epoch 5/10, Loss: 0.11855286028650072
Epoch 6/10, Loss: 0.09660988673567772
Epoch 7/10, Loss: 0.06833799017800225
Epoch 8/10, Loss: 0.05744483549561766
Epoch 9/10, Loss: 0.09223653707239363
Epoch 10/10, Loss: 0.053915230143401355
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.89 0.89 0.89 19
Pipes 1.00 0.94 0.97 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.83 0.91 12
accuracy 0.91 70
macro avg 0.93 0.92 0.91 70
weighted avg 0.93 0.91 0.92 70
Training efficientnet with lr=0.0001, batch_size=32, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.2996201316515605
Epoch 2/10, Loss: 0.7533155348565843
Epoch 3/10, Loss: 0.45471470554669696
Epoch 4/10, Loss: 0.31876489851209855
Epoch 5/10, Loss: 0.2158191916015413
Epoch 6/10, Loss: 0.14072873691717783
Epoch 7/10, Loss: 0.10812109543217553
Epoch 8/10, Loss: 0.09927617054846552
Epoch 9/10, Loss: 0.06566474338372548
Epoch 10/10, Loss: 0.057087073723475136
Accuracy: 92.86%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.91 0.95 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 1.00 1.00 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.93 70
macro avg 0.94 0.92 0.92 70
weighted avg 0.94 0.93 0.93 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.5724039793014526
Epoch 2/10, Loss: 1.3314905643463135
Epoch 3/10, Loss: 1.169212007522583
Epoch 4/10, Loss: 1.0113110423088074
Epoch 5/10, Loss: 0.8485567808151245
Epoch 6/10, Loss: 0.7145217418670654
Epoch 7/10, Loss: 0.5983829021453857
Epoch 8/10, Loss: 0.5000152230262757
Epoch 9/10, Loss: 0.43438366055488586
Epoch 10/10, Loss: 0.3416934132575989
Accuracy: 80.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.82 0.74 0.78 19
Pipes 0.93 0.82 0.88 17
Rockets 0.67 0.91 0.77 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.80 70
macro avg 0.82 0.81 0.80 70
weighted avg 0.83 0.80 0.80 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.5648975849151612
Epoch 2/10, Loss: 1.4019503355026246
Epoch 3/10, Loss: 1.2164204835891723
Epoch 4/10, Loss: 1.0896427392959596
Epoch 5/10, Loss: 0.9448953151702881
Epoch 6/10, Loss: 0.826081919670105
Epoch 7/10, Loss: 0.6879285931587219
Epoch 8/10, Loss: 0.6525192260742188
Epoch 9/10, Loss: 0.5004281818866729
Epoch 10/10, Loss: 0.4481384873390198
Accuracy: 78.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.67 0.91 0.77 11
Mines 0.87 0.68 0.76 19
Pipes 0.88 0.88 0.88 17
Rockets 0.60 0.82 0.69 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.79 70
macro avg 0.80 0.79 0.78 70
weighted avg 0.82 0.79 0.79 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6243732929229737
Epoch 2/10, Loss: 1.4917351245880126
Epoch 3/10, Loss: 1.2937013149261474
Epoch 4/10, Loss: 1.2351965665817262
Epoch 5/10, Loss: 1.1047883033752441
Epoch 6/10, Loss: 0.9791356682777405
Epoch 7/10, Loss: 0.9109376311302185
Epoch 8/10, Loss: 0.7787962794303894
Epoch 9/10, Loss: 0.6885926127433777
Epoch 10/10, Loss: 0.6274393439292908
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.55 1.00 0.71 11
Mines 0.79 0.79 0.79 19
Pipes 0.92 0.65 0.76 17
Rockets 0.90 0.82 0.86 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.77 70
macro avg 0.81 0.78 0.78 70
weighted avg 0.82 0.77 0.78 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5938063859939575
Epoch 2/10, Loss: 1.357693862915039
Epoch 3/10, Loss: 1.2100682735443116
Epoch 4/10, Loss: 1.032637071609497
Epoch 5/10, Loss: 0.8946467995643616
Epoch 6/10, Loss: 0.7937755584716797
Epoch 7/10, Loss: 0.6193403124809265
Epoch 8/10, Loss: 0.5644301056861878
Epoch 9/10, Loss: 0.4182435035705566
Epoch 10/10, Loss: 0.35708542466163634
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.71 0.91 0.80 11
Mines 0.84 0.84 0.84 19
Pipes 1.00 0.88 0.94 17
Rockets 0.57 0.73 0.64 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.81 70
macro avg 0.83 0.81 0.80 70
weighted avg 0.84 0.81 0.82 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.5785198926925659
Epoch 2/10, Loss: 1.3941497564315797
Epoch 3/10, Loss: 1.2451508283615111
Epoch 4/10, Loss: 1.1146240234375
Epoch 5/10, Loss: 0.9995124578475952
Epoch 6/10, Loss: 0.8936618447303772
Epoch 7/10, Loss: 0.7465335249900817
Epoch 8/10, Loss: 0.6178859591484069
Epoch 9/10, Loss: 0.5272130012512207
Epoch 10/10, Loss: 0.4348668098449707
Accuracy: 72.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.82 0.62 11
Mines 0.75 0.63 0.69 19
Pipes 0.87 0.76 0.81 17
Rockets 0.82 0.82 0.82 11
Vehicles 0.80 0.67 0.73 12
accuracy 0.73 70
macro avg 0.75 0.74 0.73 70
weighted avg 0.76 0.73 0.73 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.5661346197128296
Epoch 2/10, Loss: 1.4186364889144898
Epoch 3/10, Loss: 1.2654633045196533
Epoch 4/10, Loss: 1.137548565864563
Epoch 5/10, Loss: 0.984263026714325
Epoch 6/10, Loss: 0.9119868516921997
Epoch 7/10, Loss: 0.8172049999237061
Epoch 8/10, Loss: 0.6832776069641113
Epoch 9/10, Loss: 0.6084691047668457
Epoch 10/10, Loss: 0.5170246422290802
Accuracy: 77.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.50 0.91 0.65 11
Mines 0.87 0.68 0.76 19
Pipes 0.94 0.88 0.91 17
Rockets 0.73 0.73 0.73 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.77 70
macro avg 0.81 0.77 0.77 70
weighted avg 0.83 0.77 0.78 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.5777102947235107
Epoch 2/10, Loss: 1.3721783638000489
Epoch 3/10, Loss: 1.197045660018921
Epoch 4/10, Loss: 1.0305352330207824
Epoch 5/10, Loss: 0.8832461595535278
Epoch 6/10, Loss: 0.784627091884613
Epoch 7/10, Loss: 0.6453388690948486
Epoch 8/10, Loss: 0.5247297883033752
Epoch 9/10, Loss: 0.42352429032325745
Epoch 10/10, Loss: 0.3871532380580902
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.69 1.00 0.81 11
Mines 0.77 0.89 0.83 19
Pipes 1.00 0.88 0.94 17
Rockets 0.78 0.64 0.70 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.83 70
macro avg 0.85 0.82 0.82 70
weighted avg 0.85 0.83 0.83 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.563384437561035
Epoch 2/10, Loss: 1.3995598316192628
Epoch 3/10, Loss: 1.2486278772354127
Epoch 4/10, Loss: 1.1052372455596924
Epoch 5/10, Loss: 0.9769884943962097
Epoch 6/10, Loss: 0.82882639169693
Epoch 7/10, Loss: 0.7116052627563476
Epoch 8/10, Loss: 0.6376570105552674
Epoch 9/10, Loss: 0.5608766198158264
Epoch 10/10, Loss: 0.45055401921272276
Accuracy: 70.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.39 1.00 0.56 11
Mines 1.00 0.37 0.54 19
Pipes 0.88 0.88 0.88 17
Rockets 0.80 0.73 0.76 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.70 70
macro avg 0.82 0.73 0.71 70
weighted avg 0.84 0.70 0.71 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=adam, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.579128098487854
Epoch 2/10, Loss: 1.4024264335632324
Epoch 3/10, Loss: 1.2776809692382813
Epoch 4/10, Loss: 1.1523427724838258
Epoch 5/10, Loss: 1.060950469970703
Epoch 6/10, Loss: 0.9217850208282471
Epoch 7/10, Loss: 0.8320142984390259
Epoch 8/10, Loss: 0.7225804567337036
Epoch 9/10, Loss: 0.6585701942443848
Epoch 10/10, Loss: 0.5648257493972778
Accuracy: 67.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.41 1.00 0.58 11
Mines 0.80 0.42 0.55 19
Pipes 1.00 0.71 0.83 17
Rockets 0.64 0.82 0.72 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.67 70
macro avg 0.77 0.71 0.68 70
weighted avg 0.80 0.67 0.68 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.6362268447875976
Epoch 2/10, Loss: 1.6460136651992798
Epoch 3/10, Loss: 1.6179931879043579
Epoch 4/10, Loss: 1.628525733947754
Epoch 5/10, Loss: 1.6280286312103271
Epoch 6/10, Loss: 1.6044097185134887
Epoch 7/10, Loss: 1.588914966583252
Epoch 8/10, Loss: 1.5968762397766114
Epoch 9/10, Loss: 1.576203751564026
Epoch 10/10, Loss: 1.5726241588592529
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.17 0.09 0.12 11
Mines 0.43 0.16 0.23 19
Pipes 0.23 0.18 0.20 17
Rockets 0.24 0.36 0.29 11
Vehicles 0.22 0.50 0.31 12
accuracy 0.24 70
macro avg 0.26 0.26 0.23 70
weighted avg 0.27 0.24 0.23 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.6492534637451173
Epoch 2/10, Loss: 1.6165115118026734
Epoch 3/10, Loss: 1.6475290775299072
Epoch 4/10, Loss: 1.6177859783172608
Epoch 5/10, Loss: 1.6089678049087524
Epoch 6/10, Loss: 1.6076298236846924
Epoch 7/10, Loss: 1.6030371189117432
Epoch 8/10, Loss: 1.5909144163131714
Epoch 9/10, Loss: 1.5777530908584594
Epoch 10/10, Loss: 1.569985294342041
Accuracy: 18.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.18 0.27 0.21 11
Mines 0.17 0.16 0.16 19
Pipes 0.33 0.24 0.28 17
Rockets 0.12 0.18 0.15 11
Vehicles 0.14 0.08 0.11 12
accuracy 0.19 70
macro avg 0.19 0.19 0.18 70
weighted avg 0.20 0.19 0.19 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.6961760759353637
Epoch 2/10, Loss: 1.6755548954010009
Epoch 3/10, Loss: 1.6745905876159668
Epoch 4/10, Loss: 1.6487496137619018
Epoch 5/10, Loss: 1.6252707719802857
Epoch 6/10, Loss: 1.6587369441986084
Epoch 7/10, Loss: 1.6211255311965942
Epoch 8/10, Loss: 1.5944005489349364
Epoch 9/10, Loss: 1.6596249341964722
Epoch 10/10, Loss: 1.5955185174942017
Accuracy: 31.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.26 0.45 0.33 11
Mines 0.38 0.26 0.31 19
Pipes 0.45 0.53 0.49 17
Rockets 0.08 0.09 0.08 11
Vehicles 0.40 0.17 0.24 12
accuracy 0.31 70
macro avg 0.31 0.30 0.29 70
weighted avg 0.34 0.31 0.31 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6543838739395142
Epoch 2/10, Loss: 1.64377818107605
Epoch 3/10, Loss: 1.6453982591629028
Epoch 4/10, Loss: 1.6338916063308715
Epoch 5/10, Loss: 1.6309896945953368
Epoch 6/10, Loss: 1.6169009685516358
Epoch 7/10, Loss: 1.622417950630188
Epoch 8/10, Loss: 1.6003639936447143
Epoch 9/10, Loss: 1.597257423400879
Epoch 10/10, Loss: 1.578018355369568
Accuracy: 25.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.33 0.18 0.24 11
Mines 0.28 0.26 0.27 19
Pipes 0.50 0.24 0.32 17
Rockets 0.16 0.36 0.22 11
Vehicles 0.23 0.25 0.24 12
accuracy 0.26 70
macro avg 0.30 0.26 0.26 70
weighted avg 0.31 0.26 0.26 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.612135648727417
Epoch 2/10, Loss: 1.612307333946228
Epoch 3/10, Loss: 1.5852334022521972
Epoch 4/10, Loss: 1.5911762714385986
Epoch 5/10, Loss: 1.596416211128235
Epoch 6/10, Loss: 1.5852403879165649
Epoch 7/10, Loss: 1.5696961164474488
Epoch 8/10, Loss: 1.5517214059829711
Epoch 9/10, Loss: 1.5572904109954835
Epoch 10/10, Loss: 1.5344445705413818
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.21 0.36 0.27 11
Mines 0.30 0.16 0.21 19
Pipes 0.54 0.41 0.47 17
Rockets 0.15 0.27 0.19 11
Vehicles 0.25 0.17 0.20 12
accuracy 0.27 70
macro avg 0.29 0.27 0.27 70
weighted avg 0.31 0.27 0.28 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6082504749298097
Epoch 2/10, Loss: 1.628454613685608
Epoch 3/10, Loss: 1.628865361213684
Epoch 4/10, Loss: 1.6029595375061034
Epoch 5/10, Loss: 1.599972629547119
Epoch 6/10, Loss: 1.6011806011199952
Epoch 7/10, Loss: 1.5736356258392334
Epoch 8/10, Loss: 1.597867226600647
Epoch 9/10, Loss: 1.5704565525054932
Epoch 10/10, Loss: 1.571667695045471
Accuracy: 24.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.19 0.64 0.30 11
Mines 0.20 0.05 0.08 19
Pipes 0.44 0.24 0.31 17
Rockets 0.20 0.18 0.19 11
Vehicles 0.30 0.25 0.27 12
accuracy 0.24 70
macro avg 0.27 0.27 0.23 70
weighted avg 0.28 0.24 0.22 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.6348473072052
Epoch 2/10, Loss: 1.6297000885009765
Epoch 3/10, Loss: 1.6239574909210206
Epoch 4/10, Loss: 1.614413356781006
Epoch 5/10, Loss: 1.596585488319397
Epoch 6/10, Loss: 1.596467423439026
Epoch 7/10, Loss: 1.575475525856018
Epoch 8/10, Loss: 1.579884123802185
Epoch 9/10, Loss: 1.5528188943862915
Epoch 10/10, Loss: 1.5565412521362305
Accuracy: 38.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.42 0.45 0.43 11
Mines 0.27 0.21 0.24 19
Pipes 0.46 0.71 0.56 17
Rockets 0.50 0.18 0.27 11
Vehicles 0.31 0.33 0.32 12
accuracy 0.39 70
macro avg 0.39 0.38 0.36 70
weighted avg 0.38 0.39 0.36 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.638282299041748
Epoch 2/10, Loss: 1.622091245651245
Epoch 3/10, Loss: 1.6226219892501832
Epoch 4/10, Loss: 1.6080179929733276
Epoch 5/10, Loss: 1.6083444833755494
Epoch 6/10, Loss: 1.5920147180557251
Epoch 7/10, Loss: 1.5833362817764283
Epoch 8/10, Loss: 1.5603257894515992
Epoch 9/10, Loss: 1.5602335214614869
Epoch 10/10, Loss: 1.5644392251968384
Accuracy: 27.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.33 0.27 0.30 11
Mines 0.23 0.26 0.24 19
Pipes 0.31 0.24 0.27 17
Rockets 0.22 0.36 0.28 11
Vehicles 0.38 0.25 0.30 12
accuracy 0.27 70
macro avg 0.29 0.28 0.28 70
weighted avg 0.29 0.27 0.27 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=sgd, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.6007665157318116
Epoch 2/10, Loss: 1.6319330930709839
Epoch 3/10, Loss: 1.6012564659118653
Epoch 4/10, Loss: 1.6190807342529296
Epoch 5/10, Loss: 1.5880815744400025
Epoch 6/10, Loss: 1.5952600240707397
Epoch 7/10, Loss: 1.560326600074768
Epoch 8/10, Loss: 1.5603299856185913
Epoch 9/10, Loss: 1.5563519716262817
Epoch 10/10, Loss: 1.5762226343154908
Accuracy: 37.14%
Classification Report:
precision recall f1-score support
BigAnimals 0.47 0.64 0.54 11
Mines 0.40 0.42 0.41 19
Pipes 0.50 0.24 0.32 17
Rockets 0.33 0.18 0.24 11
Vehicles 0.24 0.42 0.30 12
accuracy 0.37 70
macro avg 0.39 0.38 0.36 70
weighted avg 0.40 0.37 0.36 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.0
Epoch 1/10, Loss: 1.2978243350982666
Epoch 2/10, Loss: 0.608959949016571
Epoch 3/10, Loss: 0.29138184189796446
Epoch 4/10, Loss: 0.1831474244594574
Epoch 5/10, Loss: 0.09747960269451142
Epoch 6/10, Loss: 0.0910285659134388
Epoch 7/10, Loss: 0.06399130523204803
Epoch 8/10, Loss: 0.08034069575369358
Epoch 9/10, Loss: 0.04945697113871574
Epoch 10/10, Loss: 0.029141206294298172
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 0.65 1.00 0.79 11
Mines 0.94 0.84 0.89 19
Pipes 1.00 0.88 0.94 17
Rockets 0.77 0.91 0.83 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.86 70
macro avg 0.87 0.86 0.85 70
weighted avg 0.89 0.86 0.86 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.3
Epoch 1/10, Loss: 1.3153048276901245
Epoch 2/10, Loss: 0.6628331542015076
Epoch 3/10, Loss: 0.3539230227470398
Epoch 4/10, Loss: 0.21892836391925813
Epoch 5/10, Loss: 0.17396326065063478
Epoch 6/10, Loss: 0.12162781357765198
Epoch 7/10, Loss: 0.09041286781430244
Epoch 8/10, Loss: 0.13875339478254317
Epoch 9/10, Loss: 0.07248723953962326
Epoch 10/10, Loss: 0.061784837394952774
Accuracy: 82.86%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.91 0.69 11
Mines 0.94 0.79 0.86 19
Pipes 1.00 0.88 0.94 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.83 70
macro avg 0.86 0.83 0.82 70
weighted avg 0.88 0.83 0.83 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0, dropout_rate=0.5
Epoch 1/10, Loss: 1.4187398433685303
Epoch 2/10, Loss: 0.8299294471740722
Epoch 3/10, Loss: 0.5627739191055298
Epoch 4/10, Loss: 0.40627322196960447
Epoch 5/10, Loss: 0.23339249193668365
Epoch 6/10, Loss: 0.18287723362445832
Epoch 7/10, Loss: 0.13126418441534043
Epoch 8/10, Loss: 0.11677193343639374
Epoch 9/10, Loss: 0.08075566366314887
Epoch 10/10, Loss: 0.0812554731965065
Accuracy: 85.71%
Classification Report:
precision recall f1-score support
BigAnimals 1.00 0.82 0.90 11
Mines 0.81 0.89 0.85 19
Pipes 1.00 0.82 0.90 17
Rockets 0.65 1.00 0.79 11
Vehicles 1.00 0.75 0.86 12
accuracy 0.86 70
macro avg 0.89 0.86 0.86 70
weighted avg 0.89 0.86 0.86 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.0
Epoch 1/10, Loss: 1.2887689471244812
Epoch 2/10, Loss: 0.6053781688213349
Epoch 3/10, Loss: 0.3028326153755188
Epoch 4/10, Loss: 0.1806981772184372
Epoch 5/10, Loss: 0.1216382697224617
Epoch 6/10, Loss: 0.08052903562784194
Epoch 7/10, Loss: 0.05096636712551117
Epoch 8/10, Loss: 0.046112139150500296
Epoch 9/10, Loss: 0.051368001475930215
Epoch 10/10, Loss: 0.05127584114670754
Accuracy: 91.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.79 1.00 0.88 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 1.00 1.00 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.91 70
macro avg 0.92 0.91 0.90 70
weighted avg 0.93 0.91 0.91 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.3
Epoch 1/10, Loss: 1.3558854341506958
Epoch 2/10, Loss: 0.7037025809288024
Epoch 3/10, Loss: 0.37331888675689695
Epoch 4/10, Loss: 0.24280900061130523
Epoch 5/10, Loss: 0.1811578392982483
Epoch 6/10, Loss: 0.11840059459209443
Epoch 7/10, Loss: 0.10947715342044831
Epoch 8/10, Loss: 0.072486712038517
Epoch 9/10, Loss: 0.03528739735484123
Epoch 10/10, Loss: 0.06293279007077217
Accuracy: 81.43%
Classification Report:
precision recall f1-score support
BigAnimals 0.56 0.91 0.69 11
Mines 0.88 0.79 0.83 19
Pipes 1.00 0.76 0.87 17
Rockets 0.79 1.00 0.88 11
Vehicles 1.00 0.67 0.80 12
accuracy 0.81 70
macro avg 0.84 0.83 0.81 70
weighted avg 0.86 0.81 0.82 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.0001, dropout_rate=0.5
Epoch 1/10, Loss: 1.3791187763214112
Epoch 2/10, Loss: 0.853135085105896
Epoch 3/10, Loss: 0.5374504864215851
Epoch 4/10, Loss: 0.30139556527137756
Epoch 5/10, Loss: 0.22342046797275544
Epoch 6/10, Loss: 0.17190307676792144
Epoch 7/10, Loss: 0.1147850751876831
Epoch 8/10, Loss: 0.09041808992624283
Epoch 9/10, Loss: 0.10951398238539696
Epoch 10/10, Loss: 0.08584948405623435
Accuracy: 90.00%
Classification Report:
precision recall f1-score support
BigAnimals 0.73 1.00 0.85 11
Mines 0.94 0.89 0.92 19
Pipes 1.00 1.00 1.00 17
Rockets 0.85 1.00 0.92 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.90 70
macro avg 0.90 0.90 0.88 70
weighted avg 0.92 0.90 0.90 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.0
Epoch 1/10, Loss: 1.2295215487480164
Epoch 2/10, Loss: 0.5769366264343262
Epoch 3/10, Loss: 0.2764298260211945
Epoch 4/10, Loss: 0.1757488116621971
Epoch 5/10, Loss: 0.11082994788885117
Epoch 6/10, Loss: 0.11047906279563904
Epoch 7/10, Loss: 0.053459249436855316
Epoch 8/10, Loss: 0.050970035046339034
Epoch 9/10, Loss: 0.08512213453650475
Epoch 10/10, Loss: 0.0324501920491457
Accuracy: 84.29%
Classification Report:
precision recall f1-score support
BigAnimals 0.59 0.91 0.71 11
Mines 0.94 0.84 0.89 19
Pipes 1.00 0.94 0.97 17
Rockets 0.77 0.91 0.83 11
Vehicles 1.00 0.58 0.74 12
accuracy 0.84 70
macro avg 0.86 0.84 0.83 70
weighted avg 0.88 0.84 0.85 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.3
Epoch 1/10, Loss: 1.352060580253601
Epoch 2/10, Loss: 0.720838189125061
Epoch 3/10, Loss: 0.40312455892562865
Epoch 4/10, Loss: 0.26845102310180663
Epoch 5/10, Loss: 0.14754534512758255
Epoch 6/10, Loss: 0.12166012823581696
Epoch 7/10, Loss: 0.0753995031118393
Epoch 8/10, Loss: 0.08670818880200386
Epoch 9/10, Loss: 0.06385440826416015
Epoch 10/10, Loss: 0.044777588546276094
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.83 0.91 0.87 11
Mines 0.83 1.00 0.90 19
Pipes 1.00 0.82 0.90 17
Rockets 0.92 1.00 0.96 11
Vehicles 0.89 0.67 0.76 12
accuracy 0.89 70
macro avg 0.89 0.88 0.88 70
weighted avg 0.89 0.89 0.88 70
Training efficientnet with lr=0.0001, batch_size=64, optimizer=rmsprop, weight_decay=0.001, dropout_rate=0.5
Epoch 1/10, Loss: 1.4097992181777954
Epoch 2/10, Loss: 0.8579245924949646
Epoch 3/10, Loss: 0.49915757179260256
Epoch 4/10, Loss: 0.32521591484546664
Epoch 5/10, Loss: 0.2562770336866379
Epoch 6/10, Loss: 0.18887374699115753
Epoch 7/10, Loss: 0.11365832090377807
Epoch 8/10, Loss: 0.07490110397338867
Epoch 9/10, Loss: 0.0675385594367981
Epoch 10/10, Loss: 0.05374729782342911
Accuracy: 88.57%
Classification Report:
precision recall f1-score support
BigAnimals 0.77 0.91 0.83 11
Mines 0.95 0.95 0.95 19
Pipes 1.00 1.00 1.00 17
Rockets 0.73 1.00 0.85 11
Vehicles 1.00 0.50 0.67 12
accuracy 0.89 70
macro avg 0.89 0.87 0.86 70
weighted avg 0.91 0.89 0.88 70
Best Model: efficientnet | Accuracy: 0.96% | Params: {'model': 'efficientnet', 'learning_rate': 0.0001, 'batch_size': 16, 'optimizer': 'rmsprop', 'weight_decay': 0.001, 'dropout_rate': 0.0}
Best model saved successfully!
In [13]:
# The above code block ran for 81764 seconds = 23 hours
In [14]:
torch.save(best_model, f"best_model_{best_params['model']}_complete.pth")
print("Best model saved successfully!")
Best model saved successfully!
When you save the entire model, you are saving:¶
-->The model architecture: The complete definition of the neural network, including its layers and structure.¶
-->The model parameters (weights and biases): The learned values from training.¶
The state dictionary only saves:¶
-->The model's parameters: This includes weights, biases, and any other trainable tensors.¶
Initial Individual Models -- Ignore¶
ResNet¶
In [ ]:
resnet_model = models.resnet50(weights=ResNet50_Weights.DEFAULT)
resnet_model.fc = nn.Linear(resnet_model.fc.in_features, num_classes)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(resnet_model.parameters(), lr=0.001)
print("Training and Evaluating ResNet:")
resnet_accuracy, resnet_report, resnet_cm = train_and_evaluate(
resnet_model, optimizer, criterion, train_loader, test_loader, num_epochs=10
)
VGG¶
In [ ]:
vgg_model = models.vgg16(weights=VGG16_Weights.DEFAULT)
vgg_model.classifier[6] = nn.Linear(vgg_model.classifier[6].in_features, num_classes)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(vgg_model.parameters(), lr=0.001)
print("Training and Evaluating VGG:")
vgg_accuracy, vgg_report, vgg_cm = train_and_evaluate(
vgg_model, optimizer, criterion, train_loader, test_loader, num_epochs=10
)
EfficientNet¶
In [ ]:
efficientnet_model = models.efficientnet_b0(weights=EfficientNet_B0_Weights.DEFAULT)
efficientnet_model.classifier[1] = nn.Linear(efficientnet_model.classifier[1].in_features, num_classes)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(efficientnet_model.parameters(), lr=0.001)
print("Training and Evaluating EfficientNet:")
efficientnet_accuracy, efficientnet_report, efficientnet_cm = train_and_evaluate(
efficientnet_model, optimizer, criterion, train_loader, test_loader, num_epochs=10
)
Print Results for All Models¶
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print("Summary of Results:")
print(f"ResNet Accuracy: {resnet_accuracy * 100:.2f}%")
print(f"VGG Accuracy: {vgg_accuracy * 100:.2f}%")
print(f"EfficientNet Accuracy: {efficientnet_accuracy * 100:.2f}%")
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